Phenotyping effort

Collaborative phenotyping effort of European Drosophila melanogaster populations

Genomic analyses (Kapun et al. 2020) showed that there is longitudinal population structure, continent-wide sweeps, candidate genes for local climate adaptation by using 48 pooled population samples from 32 locations.

In 2017, DrosEU consortium has decided to make a collaborative phenotyping effort of European Drosophila melanogaster populations at the Groningen meeting.

The call was made with approximate numbers of populations, lines, etc. and under predefined criteria (finish within a year, reps, blocks, etc) and participating labs have indicated which traits they are willing to phenotype.

Sampling Locations

Nine sampling locations were chosen based on genomic data and cover a wide range of latitude (~20°) and longitude (~40°) across the continent (see table below). From each location, 15 to 20 isofemale lines were established in corresponding labs at the sampling location and the isofemale lines were centrally maintained by Élio Sucena at Instituto Gulbenkian de Ciência (IGC), Lisbon, Portugal. A total of 173 isofemale lines were used in this study.

Table X. Sampling locations, coordinates and collectors, sorted by increasing latitude
Country Location Latitude Longitude Altitude Collector
Portugal Recarei 41.150 -8.410 175 Jorge Vieira
Spain Gimenells (Lleida) 41.618 0.620 173 Josefa Gonzalez
Denmark Karensminde 55.945 10.213 15 Mads Schou
Germany Munich 48.180 11.610 520 Amanda Glaser-Schmitt
Austria Mauternbach 48.375 15.560 572 Andrea Betancourt
Finland Akaa 61.100 23.520 88 Maaria Kankare
Ukraine Uman 48.753 30.206 214 Iryna Kozeretska
Turkey Yesiloz 40.231 32.260 680 Banu Onder
Russia Valday 57.979 33.244 217 Elena Pasyukova 
## Scale on map varies by more than 10%, scale bar may be inaccurate

Contributers (phenotyping)

Contributed labs are Abbott, Bergland, Billeter, Colinet, Flatt, Fricke, Gibert, Gonzalez, Grath, Hoedjes, Kozeretska, Mensch, Onder, Parsch, Pasyukova, Posnien, Ritchie, Schlötterer, Schmidt, Stamenkovic-Radak, Sucena, Tauber, Vieira, Wegener, Zwaan and detailed list below :

Table X. Contributed labs and traits, sorted by PI
Country Lab PI Trait
Sweden Lund Abbott Pigmentation
USA Charlottesville Bergland Diapause
The Netherlands Groningen Billeter Fecundity
France Rennes Colinet Dry weight
Switzerland Fribourg Flatt Diapause
Switzerland Fribourg Flatt Lifespan
Germany Muenster Fricke Fecundity
France Lyon Gibert Development time
France Lyon Gibert Pigmentation
France Lyon Gibert Viability
Spain Barcelona Gonzalez Cold-shock mortality
Spain Barcelona Gonzalez Starvation resistance
Germany Munich Grath Development time
Germany Munich Grath Viability
The Netherlands Lausanne Hoedjes Development time
The Netherlands Lausanne Hoedjes Dry weight
The Netherlands Lausanne Hoedjes Viability
Ukraine Kyiv Kozeretska Cold-shock mortality
Ukraine Kyiv Kozeretska Thorax length
Argentina Buenos Aiers Mensch Chill-coma recovery time
Turkey Ankara Onder Dry weight
Turkey Ankara Onder Starvation resistance
Turkey Ankara Onder Wing area
Germany Munich Parsch Heat-shock mortality
Germany Munich Parsch Lifespan
Russia Moscow Pasyukova Lifespan
Russia Moscow Pasyukova Starvation resistance
Germany Göttingen Posnien Thorax length
Germany Göttingen Posnien Wing area
UK St Andrews Ritchie Thorax length
UK St Andrews Ritchie Wing area
UK St Andrews Ritchie Wing patterning 
Austria Vienna Schlötterer Diapause
USA Philadelphia Schmidt Development time
USA Philadelphia Schmidt Pigmentation
USA Philadelphia Schmidt Thorax length
USA Philadelphia Schmidt Time to pupation
USA Philadelphia Schmidt Viability
Serbia Belgrade Stamenkovic-Radak Development time
Serbia Belgrade Stamenkovic-Radak Viability
Serbia Belgrade Stamenkovic-Radak Wing area
Portugal Lisbon Sucena Fly husbandry
Israel Haifa Tauber Locomotor activity
Portugal Porto Vieira Chill-coma recovery time
Portugal Porto Vieira Cold-shock mortality
Portugal Porto Vieira Heat-shock mortality
Germany Würzburg Wegener Circadian eclosion timing
The Netherlands Wageningen Zwaan Development time
The Netherlands Wageningen Zwaan Viability

Contributers (analyses)

Esra Durmaz (all), Envel Kerdaffrec (all), Katja Hoedjes (Via, DT, DW), Banu Onder (SR, WA), Marija Tanaskovic (WA), Chris Wegener (CET), Eran Tauber (LA), Rudolf Rohr (UniFr), coordination-team

Phenotyping

Overview

Methods

Wolbachia

wolbachia_pop_freqs # Wolbachia frequency by country and population abbreviation 
##    Country Population Wolbachia_Freq
## 1  Finland         AK      100.00000
## 2 Portugal         GI       66.66667
## 3  Denmark         KA       85.00000
## 4  Austria         MA       75.00000
## 5  Germany         MU       94.73684
## 6    Spain         RE       47.05882
## 7  Ukraine         UM       69.23077
## 8   Russia         VA       75.00000
## 9   Turkey         YE       85.00000

Diets

Inversion Frequencies

Traits

Contributors and methods for experiments

Viability

Gibert, Grath, Hoedjes, Schmidt, Stamenkovic-Radak and Zwaan Labs

3-5 day old adults (at least 25 pairs) are allowed to lay eggs en masse. Yeast is provided to stimulate egg laying (for at least 2 hours). Eggs are collected, and 40 are placed in each vial1. Viability is calculated per vial, as the percentage of individuals that emerged from the 40 eggs.

Please see below number of

Developmental time

Egg-to-pupa developmental time

Schmidt Lab

Egg-to-adult developmental time

Gibert, Grath, Hoedjes, Schmidt, Stamenkovic-Radak and Zwaan Labs

Developmental time is scored as both the egg-to-pupa and egg-to-adult development time. Both were scored twice a day, when the chamber lights are turned on and two hours before they are turned off. In order to measure the egg-to-pupa developmental time, the spot where a pupa is found is marked with a permanent marker to keep track of which pupae have emerged in each day. The egg-to-adult developmental time is estimated by counting all emerged adults from the vial, and by sexing them.

Dry weight

Colinet, Hoedjes and Onder Labs

At day 7 after emergence, flies are killed by snap freezing them in liquid nitrogen, by putting them at -20ºC or by putting them into an ethyl acetate solution and stored at -20ºC. Then they are sexed and placed into 96 wells plates, and placed in an oven set at 60-70 °C, for at least 3 days. At this point flies can be stored at room temperature using a protective cover. If this is the case, the day before measurements are made dry flies are again placed for 24h in the oven (60-70 °C) to ensure material is well dehydrated. Flies are then placed on a small piece of aluminium foil for direct weight measurement on microbalance (accuracy 1µg).

Thorax length

Kozeretska, Posnien, Ritchie and Schmidt Labs

Five to seven days old flies are placed onto a double-sided sticky tape attached to a microscope slide and a picture of the thorax taken using a digital camera connected to a dissecting microscope. The same magnification and resolution is always used to increase reproducibility, and a scale bar inserted on each photo to allow transforming pixels into µm units. Thorax length is defined as the distance from the anterior margin of the thorax to the posterior tip of the scutellum and it is measured using the “Straight Line” in ImageJ/Fiji.

Wing area

Onder, Posnien, Ritchie and Stamenkovic-Radak Labs

Both the left and right wings of five to seven days old flies (10 flies per sex per replicate) are removed and placed into a drop of Entellan®Merck, Hoyer’s Medium, sticked to a double-side sticky tape, or taped directly to the slide. Pictures of the wing preparations are taken using a digital camera connected to a dissecting microscope. The same magnification and resolution is always used to increase reproducibility. A scale bar is placed on each photo to allow transforming pixels into µm units. Manual measurements of wing length and wing area are performed using the “Straight Line” and “Polygon Selection” tools, respectively of ImageJ/Fiji (10.1371/journal.pone.0000007).

Fecundity

Billeter and Fricke Labs

For each isofemale line, 10 males and 10 females are placed together in single-sex groups and allowed to mature for five days. Then, they are placed together (5-7 pairs), and mating interactions observed to ensure successful mating (at least 10 min copulation duration) to ensure that we have five successfully mated females. After a successful mating, males are discarded and females allowed to oviposit alone for 48 hours, moved to another vial, and allowed to oviposit for four days, and again moved to another vial and allowed to oviposit for two days to check that of egg-laying stopped. Vials are incubated until all offspring is born. Individuals are then frozen and the offspring counted.

Lifespan

Flatt, Parsch and Pasyukova Labs

Line level lifespan: Ten flies per sex/line are placed in each vial. The age at death will be scored when changing the food, at least three times a week. Five replicates are used.

Population level lifespan: Flies are kept in 1L demography cages (5 flies per line/sex for each population). The age at death will be scored when changing the food, at least three times a week. Ten replicates were performed.

Cold-shock mortality

Gonzalez, Kozeretska and Vieira Labs

Batches of 15-20 seven days old flies are placed for 18 hours in an empty vial immersed in an ice-water slurry box placed at a 4°C room for 18 hours. Then the vials are removed to a bench in a 25°C room and mortality scored 24 hours later.

Chill-coma recovery time

Vieira and Mensh Labs

Sexed flies are placed in an empty vial immersed in an ice-water slurry box placed at a 4°C room in the morning. Six hours later, flies are removed from the tube to individual wells of 24 well plates while being kept on ice. A timer is started once the plate is moved from the ice to a bench in a 25°C room. Each fly is checked by eye for recovery for a maximum of 60 minutes. Flies that are able to stand on their legs are considered recovered and the CCRT (in seconds) recorded.

Heat-shock mortality

Parsch and Vieira Labs

Batches of 15-20 seven days old flies are placed in empty vials inside a 37ºC incubator and mortality checked for 7 hours every 30 minutes.

Diapause

Bergland, Flatt and Schlotterer Labs

In order to induce diapause, two hour old ‘phenotypic virgins’ (pharate or melanized with meconium visible) female flies are exposed to 12°C and 10:14 light/dark hours for 3 weeks, using an incubator that allows temperature tracking. Vials are changed once per week. After three weeks under diapause conditions, flies are frozen at -80C until dissection. Both ovaries will be examined and classified according to the following simplified ‘classification’: 1) < stage 10: diapause; 2) stage 10-13: intermediate; 3) stage 14: non-diapause.

  • Standard diapause conditions: 12°C at 10:14 hrs L:D (under these conditions we might expect around 50% dipause frequency)
  • duration of exposure: 3 weeks
  • assays will be done in incubators (Percival or Memmert); temperature conditions will be tracked using data loggers / iButtons
  • Try to have haphazard standardization; similar conditions coupled with consistent tracking of temperature (multiple iButtons, for example)
  • We will use egg lays in bottles to be able to collect more flies for the assay (approx. 1-2 bottles per line)
  • Larval density will be controlled (approximately) prior to assays: we will do this by letting a fixed number of (approx. 10-20) females (housed together with 5-10 males) oviposit over night (fixed time window) - how many females and the duration of egg laying might need to be adjusted a bit depending on the fertility of the lines (to be determined when lines are being amplified prior to assays)
  • The Bergland and Flatt labs will standardize their fly food according to the Gulbenkian recipe (in terms of amounts / proportions); Manolis from the Schlötterer lab will not standardize the food and use their local diet; importantly, flies for the assays will not be exposed to any (additional) live yeast (i.e., the diet will contain liophilized yeast but no live yeast sprinkled on top).
  • From the bottles we will collect upon eclosion ‘phenotypic virgins’ (=pharate adults or melanized with meconium visible), approx. within 2 hours after eclosion
  • Flies will be kept in vials for assay (15-20 flies in the same vial to account for mortality); we aim to measure a minimum of 10 females per line (from approx. 10-15 lines x 10 populations, yielding about 1000-1500 flies for dissections in total)
  • The food in the assays will be changed once per week
  • To keep the workload and effort manageable the assays will be performed in 2-3 blocks (optimally only 2) over time: e.g., from each population we will measure a subset of all lines (e.g., 5-7 lines, i.e. 1/2 or 1/3 of all lines) in each of the 2-3 blocks.
  • After 3 weeks under diapause conditions, flies will either be dissected or kept frozen until dissection (dry, at -80C; can be stored for a few months)
  • We will adopt a simplified diapause ‘classification’:
  • Stage 10 will be defined as the checkpoint
  • < Stage 10: diapause
  • Stage 10-13: intermediate
  • Stage 14: non-diapause
  • We will examine both ovaries.

Circadian eclosion timing

Wegener Lab

The eclosion rhythmicity has been measured in outcrossed Munich, Akaa, Gimenells, Uman, Mauternbach, Karensminde, Valday, Yesiloz and Recarei strains at:

  1. LD14:10 @ 29°C, flies raised at 29°C

  2. DD 29°C after LD14:10 entrainment, flies raised at 29°C

  3. LD14:10 @ 18°C, flies raised at 18°C

  4. DD 18°C after LD14:10 entrainment, flies raised at 18°C

Locomotor activity

Tauber Lab

The locomotor activity of the flies (males only) was measured using the DAM2 Drosophila monitors (Trikinetics Inc., Waltham, MA). Flies were 1-3 days old. Single flies were placed in glass tubes (10 cm × 0.5 cm) that were filled with 2 cm sugar/agar medium. The monitors were placed in light chambers driven by LED, in an incubator at 24°C, ~30% humidity. The flies were entrained to a light-dark cycle (LD 12:12) for 5d and then allowed to free-run for 10 d in constant darkness (DD). The activity data were processed into 30 min bins, and four different variables were analysed. These included the circadian period, and the phase, which were analyzed using the FFT NLLS algorithm available at the BioDare2 server (https://biodare2.ed.ac.uk/). The other two variables, level of activity and the nocturnal/diurnal ratio were analyzed by a custom-made R script.

Starvation resistance

Gonzalez, Onder and Pasyukova Labs

Batches of 10 sexed flies are transferred to glass vials filled with 5 mL of 2% agar for starvation 3-7 days after eclosion. The age at death will be scored every 8 hours.

Pigmentation

Abbott, Gibert and Schmidt

For each line, 10 females, 13-15 days old, either alive or stored in 95% ethanol, are air dried and placed on their left side, and pictures taken using a dissecting microscope. Images are then analysed in ImageJ 1.46r, using the Area Fraction measurement in the Analyze menu. Area Fraction measures the percentage of pixels in a selected area that have been highlighted in red using the Threshold tool. This gives an estimate of the percentage of dark pigmentation on the three terminal tergites of the abdomen (4, 5 and 6).

Data reformatting

MasterSheets

All data files are reformatted as so called Master Sheets , in which the data structure is the same for all the sub-data sets and all details/edits logged in a README sheet.

Table X. Filling in the data spreadsheet and header description
Headers Descriptions
Supervisor/PI Lab identifier
Diet Either standardized S or non-standardized NS
Batch From 1 to n
Population Population identifier in abbreviated form (i.e. AK)
Line Line identifier (i.e. MU1)
Sex Either female (F) or male (M)
ReplicateVial Replicate identifier (from 1 to n)
TraitValue Trait value in x units 
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

Data check

For each trait, sex and lab, we run counts for population, line and replicate vial (if applicable).

insert count table

Results I : Linear Models

text BLA BLA BLA

Examples & Directories & Legends

Tables

Here is an example code for tables (egg-to-adult viability). The very same code is used for other traits (either with or without Batch) by filtering the data at supervisor, batch, population and/or line levels (and also for sex, if applicable).

## First defined standard error and coefficient of variation

std_err <- function(x) sd(x)/sqrt(length(x))
coef_var <- function(x) sd(x)/mean(x)
estimate_mode <- function(v) {
   uniqv <- unique(v)
   uniqv[which.max(tabulate(match(v, uniqv)))]
}
# Then compute descriptive statistics (mean, median, minimum, maximum, standard error, coefficient of variation and mode) at the defined levels (line with batch information example below) : 

table_Via_Line_wbatch <- write.csv(d_Via %>% group_by(Supervisor.PI, Batch, Population, Line) %>% summarise_at(vars(ProportionEggtoAdultSurvival), list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, SE = std_err, CV = coef_var, Mode = estimate_mode)), file = "Viability/table_Via_Line_wbatch.csv", row.names = T)

table_Via_Line_wbatch <- read.csv("Viability/table_Via_Line_wbatch.csv") #file name is here

Tables for descriptive statistics at population and line levels can be found in the trait directory, under the file name table_TraitAbbreviation_Level_BatchInfo.csv (i.e. Viability/table_Via_Line_wobatch.csv)

Figures

Data range is calculated with #r min() and #r max() functions for each trait.

y-axis on the figures is scaled by the minimum (#r min_trait) and maximum (#r max_trait) values in the full data set for a given trait.

The very same ggplot theme has been used all across the document, called droseu_theme

droseu_theme <- theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black",),  axis.title.x = element_text(size = 16), axis.text.x = element_text(size = 16),axis.text.y = element_text(size = 16),axis.title.y = element_text(size = 16))

p_TraitAbbreviation_Lab + ylim(c(min_trait, max_triat)) + droseu_theme

Here is an example code for figures (egg-to-adult viabiliy, Gibert Lab). The very same code is used to generate plots for other contributing labs and traits (either for Batch or Population) by filtering the data at supervisor level (for females and males separately, if applicable).

p_Via_Gibert <- ggplot(data = (subset(d_Via,Supervisor.PI=='Gibert')), #subsets for supervisor
                       aes(x=Population_Lat, y=ProportionEggtoAdultSurvival, fill=Batch)) + #define x- and y-axis
                       geom_boxplot(outlier.shape = NA, notch=FALSE) +  #boxplot
                       labs(title="p_Via_Gibert", x="Population", y = "ProportionEggtoAdultSurvival") + 
                      ylim(c(min_Via, max_Via)) + 
                      droseu_theme #y-axis limits, axis labels and droseu_theme

pdf(file="Viability/p_Via_Gibert.pdf",width=8, height=5)

p_Via_Gibert

invisible(dev.off())

Linear Models

Here is an example code for linear models (egg-to-adult development time, males, Gibert Lab). The very same code is used for other contributing labs and traits by filtering the data at supervisor level (for females and males separately, if applicable), adding or removing factors if they cause convergence problems or singularity (i.e. if only one replicate vial has been phenotyped per line and/or population and/or batch, the lowest level [ replicate_vial in this examplstae] has been dropped from given model).

DT_A_M_lmer_Gibert <- lmer(DT_EggAdult ~ Population + (1|Line:Population) + #Line (random) is nested in Population (fixed)
                             (1|Batch) + #Batch is a random effect 
                             (1|ReplicateVial : Line), #Replicate vial (random) is nested in Line (random)
                           data = d_DT_A_M[d_DT_A_M$Supervisor.PI == "Gibert",]) #Filter by Lab in development time, males data 
capture.output(summary(DT_A_M_lmer_Gibert),file = "DevelopmentTime/DT_A_M_lmer_Gibert_sum.txt") #save output summary 
capture.output(anova(DT_A_M_lmer_Gibert),file = "DevelopmentTime/DT_A_M_lmer_Gibert.txt") #save anova output 
capture.output(emmeans(DT_A_M_lmer_Gibert, list(pairwise ~ Population), adjust = "tukey", mode = "asymp"),file =  "DevelopmentTime/DT_A_M_lmer_Gibert_tk.txt") #run and save post-hoc test (tukey) output 
anova(DT_A_M_lmer_Gibert) #print anova output
summary(DT_A_M_lmer_Gibert) #print model output summary

Here is an example code for linear models for geography (altitude, dry weight, males, Onder Lab). The very same code is used for other contributing labs and traits by filtering the data at supervisor level (for females and males separately, if applicable), adding or removing factors if they cause convergence problems or singularity (i.e. if only one replicate vial has been phenotyped per line and/or population and/or batch, the lowest level [ replicate_vial in this example] has been dropped from given model) and also for latitude and longitude.

DW_M_lmer_Alt_Onder <- lmer(DW_micrograms ~ Altitude + (1|Population) + (1|Population:Line) + (1|Batch), data = d_DW_M[d_DW_M$Supervisor.PI == "Onder",])
capture.output(summary(DW_M_lmer_Alt_Onder),file = "DryWeight/DW_M_lmer_Alt_Onder_sum.txt")
capture.output(anova(DW_M_lmer_Alt_Onder),file = "DryWeight/DW_M_lmer_Alt_Onder.txt")

Survival Models

Here is an example code for survival analyses (starvation resistance, females, Gonzalez Lab). The very same code is used for other contributing labs and traits by filtering the data at supervisor level and sex. Please note that the level “replicate_vial” has been dropped from all survival analyses, as it clogs the models due to the volume of data.

SR_F_coxme_Gonzalez <- coxme(Surv(AgeAtDeath_hours, Censor) ~ Population + (1|Batch) + (1|Population/Line) , data = filter(d_SR_surv, Supervisor.PI == "Gonzalez", Sex == "F"))
capture.output(summary(SR_F_coxme_Gonzalez), file = "Starvation/SR_F_coxme_Gonzalez_sum.txt")
capture.output(anova(SR_F_coxme_Gonzalez), file = "Starvation/SR_F_coxme_Gonzalez.txt")

Analyses outputs can be found in the trait directory, under the file name TraitAbbreviation_Sex_Function_LabIdentifier.txt (i.e. DevelopmentTime/DT_A_M_lmer_Gibert.txt)

Please note that “Plots and Linear Models by Lab” are presented in alphabetical order.

Viability

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Gibert Lab :Cristina Vieira, Laurence Mouton, Natacha Kremer, Sonia Martinez, Patricia Gibert

Grath Lab : Ingo Müller, Sonja Grath

Hoedjes Lab : Hristina Kostic, Katja Hoedjes

Schmidt Lab : Ozan Kiratli, Yonatan Babore, Liam Forsythe, Paul Schmidt

Stamenkovic-Radak Lab : Marija Savic Veselinovic, Marija Tanaskovic, Aleksandra Patenkovic, Mihailo Jelic, Katarina Eric, Pavle Eric, Slobodan Davidovic, Marina Stamenkovic-Radak

Zwaan Lab : Joost van den Heuvel, Bas Zwaan

Data reformatting

Reading data in R

d_Via <- read.csv("MasterSheets_Oct21_git/VIA_MasterSheet_Oct21.csv")
str(d_Via)
## 'data.frame':    2367 obs. of  12 variables:
##  $ Supervisor.PI               : chr  "Gibert" "Gibert" "Gibert" "Gibert" ...
##  $ Diet                        : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch                       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population                  : chr  "AK" "AK" "AK" "AK" ...
##  $ Line                        : chr  "AK1" "AK1" "AK1" "AK10" ...
##  $ ReplicateVialOld            : int  1 2 3 1 2 3 1 2 3 1 ...
##  $ ReplicateVial               : chr  "Gibert_1_AK1_1" "Gibert_1_AK1_2" "Gibert_1_AK1_3" "Gibert_1_AK10_1" ...
##  $ ProportionEggtoAdultSurvival: num  0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
##  $ Country                     : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude                    : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude                   : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude                    : int  88 88 88 88 88 88 88 88 88 88 ...

Factors need reformatting (i.e. Supervisor.PI should be coded as a factor, not character).

str(d_Via)
## 'data.frame':    2367 obs. of  12 variables:
##  $ Supervisor.PI               : chr  "Gibert" "Gibert" "Gibert" "Gibert" ...
##  $ Diet                        : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch                       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population                  : chr  "AK" "AK" "AK" "AK" ...
##  $ Line                        : chr  "AK1" "AK1" "AK1" "AK10" ...
##  $ ReplicateVialOld            : int  1 2 3 1 2 3 1 2 3 1 ...
##  $ ReplicateVial               : chr  "Gibert_1_AK1_1" "Gibert_1_AK1_2" "Gibert_1_AK1_3" "Gibert_1_AK10_1" ...
##  $ ProportionEggtoAdultSurvival: num  0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
##  $ Country                     : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude                    : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude                   : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude                    : int  88 88 88 88 88 88 88 88 88 88 ...
d_Via$Supervisor.PI <- as.factor(d_Via$Supervisor.PI)
d_Via$Diet <- as.factor(d_Via$Diet)
d_Via$Batch <- as.factor(d_Via$Batch)
d_Via$Population_Lat <- factor(d_Via$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Via$Population_Lon <- factor(d_Via$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Via$Population_Alt <- factor(d_Via$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Via$Line <- as.factor(d_Via$Line)
d_Via$ReplicateVial <- as.factor(d_Via$ReplicateVial)
d_Via$ProportionEggtoAdultSurvival <- as.numeric(d_Via$ProportionEggtoAdultSurvival)
d_Via$Country <- as.factor(d_Via$Country)
d_Via$Latitude <- as.numeric(d_Via$Latitude)
d_Via$Longitude <- as.numeric(d_Via$Longitude)
d_Via$Altitude <- as.numeric(d_Via$Altitude)

# Now they should be in the correct format, see below. 
str(d_Via)
## 'data.frame':    2367 obs. of  15 variables:
##  $ Supervisor.PI               : Factor w/ 6 levels "Gibert","Grath",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet                        : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch                       : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population                  : chr  "AK" "AK" "AK" "AK" ...
##  $ Line                        : Factor w/ 172 levels "AK1","AK10","AK11",..: 1 1 1 2 2 2 4 4 4 9 ...
##  $ ReplicateVialOld            : int  1 2 3 1 2 3 1 2 3 1 ...
##  $ ReplicateVial               : Factor w/ 2367 levels "Gibert_1_AK1_1",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ ProportionEggtoAdultSurvival: num  0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
##  $ Country                     : Factor w/ 9 levels "Austria","Denmark",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ Latitude                    : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude                   : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude                    : num  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat              : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon              : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt              : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
# Voila!

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_Via <- min(d_Via$ProportionEggtoAdultSurvival)
max_Via <- max(d_Via$ProportionEggtoAdultSurvival)

y-axis is scaled by the minimum (0) and maximum (1) values in the full data set.

Gibert Lab

anova(Via_lmer_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 0.80451 0.10056     8 153.66  8.0873 4.389e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Gibert)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +  
##     (1 | Batch)
##    Data: d_Via_trans[d_Via_trans$Supervisor.PI == "Gibert", ]
## 
## REML criterion at convergence: -547.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.1956 -0.5255 -0.0195  0.5213  2.8588 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.012426 0.11147 
##  Batch           (Intercept) 0.000125 0.01118 
##  Residual                    0.012435 0.11151 
## Number of obs: 532, groups:  Line:Population, 169; Batch, 3
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    1.06673    0.02957  27.03333  36.074  < 2e-16 ***
## PopulationGI  -0.16344    0.04382 154.67733  -3.730 0.000269 ***
## PopulationKA  -0.04174    0.04065 155.52140  -1.027 0.306077    
## PopulationMA  -0.12876    0.04058 153.42366  -3.173 0.001822 ** 
## PopulationMU  -0.04274    0.04059 154.79040  -1.053 0.293955    
## PopulationRE  -0.12273    0.04311 155.47126  -2.847 0.005017 ** 
## PopulationUM  -0.05047    0.04169 154.54796  -1.211 0.227874    
## PopulationVA  -0.14130    0.04059 154.79040  -3.481 0.000649 ***
## PopulationYE  -0.26929    0.04059 154.79040  -6.634 5.16e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.631                                                 
## PopulatinKA -0.681  0.463                                          
## PopulatinMA -0.684  0.463  0.500                                   
## PopulatinMU -0.682  0.463  0.500  0.500                            
## PopulatinRE -0.642  0.437  0.470  0.470  0.471                     
## PopulatinUM -0.664  0.451  0.487  0.487  0.487  0.458              
## PopulatinVA -0.682  0.463  0.500  0.500  0.501  0.471  0.487       
## PopulatinYE -0.682  0.463  0.500  0.500  0.501  0.471  0.487  0.501

Grath Lab

anova(Via_lmer_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Population 0.077956 0.038978     2 27.308  1.9446 0.1624
summary(Via_lmer_Grath)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population)
##    Data: d_Via_trans[d_Via_trans$Supervisor.PI == "Grath", ]
## 
## REML criterion at convergence: -123.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1670 -0.5714  0.0117  0.4894  2.9754 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.004157 0.06447 
##  Residual                    0.020045 0.14158 
## Number of obs: 147, groups:  Line:Population, 30
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   0.98774    0.02906 28.44269  33.995   <2e-16 ***
## PopulationMU -0.07674    0.04075 27.59627  -1.883   0.0703 .  
## PopulationRE -0.05958    0.04075 27.59627  -1.462   0.1551    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltMU
## PopulatinMU -0.713       
## PopulatinRE -0.713  0.508

Hoedjes Lab

anova(Via_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF DenDF F value    Pr(>F)    
## Population 0.51582 0.064478     8   158  6.2599 4.985e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +  
##     (1 | Batch)
##    Data: d_Via_trans[d_Via_trans$Supervisor.PI == "Hoedjes", ]
## 
## REML criterion at convergence: -549.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3516 -0.5255 -0.0429  0.4980  4.2544 
## 
## Random effects:
##  Groups          Name        Variance  Std.Dev. 
##  Line:Population (Intercept) 1.545e-02 1.243e-01
##  Batch           (Intercept) 4.088e-12 2.022e-06
##  Residual                    1.030e-02 1.015e-01
## Number of obs: 501, groups:  Line:Population, 167; Batch, 4
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    1.09520    0.03073 157.99981  35.644  < 2e-16 ***
## PopulationGI  -0.20137    0.04693 157.99994  -4.290 3.10e-05 ***
## PopulationKA  -0.06612    0.04345 157.99994  -1.522 0.130106    
## PopulationMA  -0.08792    0.04345 157.99994  -2.023 0.044720 *  
## PopulationMU  -0.04924    0.04345 157.99994  -1.133 0.258863    
## PopulationRE  -0.17978    0.04693 157.99994  -3.831 0.000184 ***
## PopulationUM  -0.10841    0.04533 157.99994  -2.392 0.017952 *  
## PopulationVA  -0.11483    0.04345 157.99994  -2.643 0.009052 ** 
## PopulationYE  -0.24862    0.04345 157.99994  -5.722 5.15e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655                                                 
## PopulatinKA -0.707  0.463                                          
## PopulatinMA -0.707  0.463  0.500                                   
## PopulatinMU -0.707  0.463  0.500  0.500                            
## PopulatinRE -0.655  0.429  0.463  0.463  0.463                     
## PopulatinUM -0.678  0.444  0.479  0.479  0.479  0.444              
## PopulatinVA -0.707  0.463  0.500  0.500  0.500  0.463  0.479       
## PopulatinYE -0.707  0.463  0.500  0.500  0.500  0.463  0.479  0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

Schmidt Lab

anova(Via_lm_Schmidt) #lm()` is used only for Schmidt Lab's viability data, as only one vial per line was phenotyped 
## Analysis of Variance Table
## 
## Response: ProportionEggtoAdultSurvival
##             Df  Sum Sq  Mean Sq F value   Pr(>F)   
## Population   8  1.7653 0.220666  2.6999 0.008308 **
## Residuals  153 12.5050 0.081732                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lm_Schmidt)
## 
## Call:
## lm(formula = ProportionEggtoAdultSurvival ~ Population, data = d_Via_trans[d_Via_trans$Supervisor.PI == 
##     "Schmidt", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73241 -0.21504  0.01621  0.15661  0.83839 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.92028    0.06393  14.396   <2e-16 ***
## PopulationGI -0.18787    0.09765  -1.924   0.0562 .  
## PopulationKA  0.21332    0.09041   2.360   0.0196 *  
## PopulationMA  0.01281    0.09288   0.138   0.8905    
## PopulationMU -0.03180    0.09041  -0.352   0.7255    
## PopulationRE  0.02327    0.09765   0.238   0.8120    
## PopulationUM  0.10329    0.09962   1.037   0.3015    
## PopulationVA -0.03068    0.09041  -0.339   0.7348    
## PopulationYE -0.08020    0.09041  -0.887   0.3764    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2859 on 153 degrees of freedom
## Multiple R-squared:  0.1237, Adjusted R-squared:  0.07789 
## F-statistic:   2.7 on 8 and 153 DF,  p-value: 0.008308

p_Via_pop_Schmidt

Stamenkovic-Radak Lab

anova(Via_lmer_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 0.50121 0.062652     8 155.29  5.2001 9.104e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_StamenkovicRadak)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +  
##     (1 | Batch)
##    Data: d_Via_trans[d_Via_trans$Supervisor.PI == "StamenkovicRadak",      ]
## 
## REML criterion at convergence: -485.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0263 -0.5091 -0.0167  0.5112  3.1782 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.015859 0.1259  
##  Batch           (Intercept) 0.001318 0.0363  
##  Residual                    0.012048 0.1098  
## Number of obs: 501, groups:  Line:Population, 167; Batch, 4
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    0.98823    0.03644  24.11360  27.117  < 2e-16 ***
## PopulationGI  -0.09533    0.05031 155.41420  -1.895  0.05998 .  
## PopulationKA  -0.04514    0.04464 155.26320  -1.011  0.31344    
## PopulationMA  -0.14459    0.04460 155.11506  -3.242  0.00145 ** 
## PopulationMU   0.01274    0.04460 155.09434   0.286  0.77548    
## PopulationRE  -0.07059    0.04737 155.37260  -1.490  0.13819    
## PopulationUM  -0.10798    0.04589 155.37865  -2.353  0.01987 *  
## PopulationVA  -0.13926    0.04460 155.11479  -3.122  0.00214 ** 
## PopulationYE  -0.21115    0.04464 155.26320  -4.730    5e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.542                                                 
## PopulatinKA -0.614  0.443                                          
## PopulatinMA -0.611  0.442  0.499                                   
## PopulatinMU -0.613  0.442  0.500  0.500                            
## PopulatinRE -0.579  0.415  0.472  0.471  0.472                     
## PopulatinUM -0.598  0.429  0.488  0.486  0.487  0.460              
## PopulatinVA -0.612  0.445  0.500  0.499  0.500  0.470  0.485       
## PopulatinYE -0.614  0.443  0.501  0.499  0.500  0.472  0.488  0.500

Zwaan Lab

anova(Via_lmer_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 1.6727 0.20908     8 150.05  6.3093 4.877e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Zwaan)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Batch) + (1 |  
##     Line:Population)
##    Data: d_Via_trans[d_Via_trans$Supervisor.PI == "Zwaan", ]
## 
## REML criterion at convergence: -124.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5670 -0.4679  0.0236  0.4732  2.8902 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.01407  0.1186  
##  Batch           (Intercept) 0.00000  0.0000  
##  Residual                    0.03314  0.1820  
## Number of obs: 524, groups:  Line:Population, 169; Batch, 2
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    1.072023   0.035162 146.338135  30.488  < 2e-16 ***
## PopulationGI  -0.134116   0.053744 145.530211  -2.495   0.0137 *  
## PopulationKA   0.006813   0.049907 147.722344   0.137   0.8916    
## PopulationMA  -0.045478   0.049950 148.488248  -0.910   0.3640    
## PopulationMU  -0.053131   0.050371 151.671972  -1.055   0.2932    
## PopulationRE  -0.122316   0.052526 144.039216  -2.329   0.0213 *  
## PopulationUM   0.033358   0.051199 147.170696   0.652   0.5157    
## PopulationVA  -0.019751   0.050311 151.428483  -0.393   0.6952    
## PopulationYE  -0.258184   0.050259 151.942216  -5.137 8.44e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.654                                                 
## PopulatinKA -0.705  0.461                                          
## PopulatinMA -0.704  0.461  0.496                                   
## PopulatinMU -0.698  0.457  0.492  0.491                            
## PopulatinRE -0.669  0.438  0.472  0.471  0.467                     
## PopulatinUM -0.687  0.449  0.484  0.483  0.479  0.460              
## PopulatinVA -0.699  0.457  0.492  0.492  0.488  0.468  0.480       
## PopulatinYE -0.700  0.458  0.493  0.492  0.488  0.468  0.480  0.489
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
#could drop batch, as it explains nothing

Lab correlations

There are 3 populations and 29 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Developmental time

Egg-to-pupa developmental time

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Schmidt Lab : Paul Schmidt

Data Reformatting

Reading data in R

d_DT_P <- read.csv("MasterSheets_Oct21_git/DTP_MasterSheet_Oct21.csv")
str(d_DT_P) #Factors need reformatting 
## 'data.frame':    3391 obs. of  13 variables:
##  $ Supervisor.PI   : chr  "Schmidt" "Schmidt" "Schmidt" "Schmidt" ...
##  $ Diet            : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch           : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population      : chr  "VA" "VA" "VA" "VA" ...
##  $ Line            : chr  "VA35" "VA35" "VA35" "VA35" ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : chr  "Schmidt_1_VA35_1" "Schmidt_1_VA35_1" "Schmidt_1_VA35_1" "Schmidt_1_VA35_1" ...
##  $ Individual      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ DT_EggPupa      : int  120 120 120 120 136 136 136 136 136 136 ...
##  $ Country         : chr  "Russia" "Russia" "Russia" "Russia" ...
##  $ Latitude        : num  58 58 58 58 58 ...
##  $ Longitude       : num  33.2 33.2 33.2 33.2 33.2 ...
##  $ Altitude        : int  217 217 217 217 217 217 217 217 217 217 ...
d_DT_P$Supervisor.PI <- as.factor(d_DT_P$Supervisor.PI)
d_DT_P$Diet <- as.factor(d_DT_P$Diet)
d_DT_P$Batch <- as.factor(d_DT_P$Batch)
d_DT_P$Population_Lat <- factor(d_DT_P$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_DT_P$Population_Lon <- factor(d_DT_P$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_DT_P$Population_Alt <- factor(d_DT_P$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_DT_P$Line <- as.factor(d_DT_P$Line)
d_DT_P$ReplicateVial <- as.factor(d_DT_P$ReplicateVial)
d_DT_P$DT_EggPupa <- as.numeric(d_DT_P$DT_EggPupa)
str(d_DT_P) #They should be ok now. 
## 'data.frame':    3391 obs. of  16 variables:
##  $ Supervisor.PI   : Factor w/ 1 level "Schmidt": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet            : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch           : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population      : chr  "VA" "VA" "VA" "VA" ...
##  $ Line            : Factor w/ 161 levels "AK1","AK10","AK11",..: 131 131 131 131 131 131 131 131 131 131 ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : Factor w/ 161 levels "Schmidt_1_AK1_1",..: 131 131 131 131 131 131 131 131 131 131 ...
##  $ Individual      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ DT_EggPupa      : num  120 120 120 120 136 136 136 136 136 136 ...
##  $ Country         : chr  "Russia" "Russia" "Russia" "Russia" ...
##  $ Latitude        : num  58 58 58 58 58 ...
##  $ Longitude       : num  33.2 33.2 33.2 33.2 33.2 ...
##  $ Altitude        : int  217 217 217 217 217 217 217 217 217 217 ...
##  $ Population_Lat  : Factor w/ 9 levels "YE","RE","GI",..: 8 8 8 8 8 8 8 8 8 8 ...
##  $ Population_Lon  : Factor w/ 9 levels "RE","GI","KA",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Alt  : Factor w/ 9 levels "KA","AK","GI",..: 6 6 6 6 6 6 6 6 6 6 ...

Descriptive Statistics

# Note that the trait has been phenotyped only in Schmidt lab and in one batch.

Descriptive statistics at the line level :

Descriptive statistics at the population level:

Plots and Linear Models by Lab

min_DT_P <- min(d_DT_P$DT_EggPupa)
max_DT_P <- max(d_DT_P$DT_EggPupa)

y-axis is scaled by the minimum (96) and maximum (192) values in the full data set.

Schmidt Lab
anova(DT_P_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population 2515.1  314.38     8 147.95  2.6412 0.009804 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_P_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggPupa ~ Population + (1 | Population:Line)
##    Data: d_DT_P
## 
## REML criterion at convergence: 26303.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9734 -0.6092 -0.0831  0.3912  4.7796 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Population:Line (Intercept) 158.4    12.59   
##  Residual                    119.0    10.91   
## Number of obs: 3391, groups:  Population:Line, 161
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  127.5020     2.8668 143.5783  44.475  < 2e-16 ***
## PopulationGI  12.9203     4.7140 157.7547   2.741  0.00684 ** 
## PopulationKA  -0.2098     4.0656 144.7480  -0.052  0.95892    
## PopulationMA   4.5016     4.1179 144.9709   1.093  0.27613    
## PopulationMU   2.2279     4.0527 143.3696   0.550  0.58337    
## PopulationRE   7.7677     4.4068 146.8919   1.763  0.08004 .  
## PopulationUM   3.6588     4.3705 142.4818   0.837  0.40391    
## PopulationVA  13.2633     4.1248 145.8807   3.215  0.00160 ** 
## PopulationYE   7.6982     4.0711 145.8355   1.891  0.06062 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.608                                                 
## PopulatinKA -0.705  0.429                                          
## PopulatinMA -0.696  0.423  0.491                                   
## PopulatinMU -0.707  0.430  0.499  0.492                            
## PopulatinRE -0.651  0.396  0.459  0.453  0.460                     
## PopulatinUM -0.656  0.399  0.463  0.457  0.464  0.427              
## PopulatinVA -0.695  0.423  0.490  0.484  0.492  0.452  0.456       
## PopulatinYE -0.704  0.428  0.497  0.490  0.498  0.458  0.462  0.489
p_DT_P_Schmidt

Egg-to-adult developmental time

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Gibert Lab : Cristina Vieira, Laurence Mouton, Natacha Kremer, Sonia Martinez, Patricia Gibert

Grath Lab : Ingo Müller, Sonja Grath

Hoedjes Lab : Hristina Kostic, Katja Hoedjes

Schmidt Lab : Ozan Kiratli, Yonatan Babore, Liam Forsythe, Paul Schmidt

Stamenkovic-Radak Lab : Marija Savic Veselinovic, Marija Tanaskovic, Aleksandra Patenkovic, Mihailo Jelic, Katarina Eric, Pavle Eric, Slobodan Davidovic, Marina Stamenkovic-Radak

Zwaan Lab : Joost van den Heuvel, Bas Zwaan

Data Reformatting

d_DT_A <- read.csv("MasterSheets_Oct21_git/DTA_MasterSheet_Oct21.csv")
str(d_DT_A) #Factors need reformatting 
## 'data.frame':    57609 obs. of  14 variables:
##  $ Supervisor.PI   : chr  "Gibert" "Gibert" "Gibert" "Gibert" ...
##  $ Diet            : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch           : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Sex             : chr  "F" "F" "F" "F" ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : chr  "Gibert_1_AK1_1" "Gibert_1_AK1_1" "Gibert_1_AK1_1" "Gibert_1_AK1_1" ...
##  $ Individual      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ DT_EggAdult     : num  202 202 202 202 202 202 202 202 202 202 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
d_DT_A$Supervisor.PI <- as.factor(d_DT_A$Supervisor.PI)
d_DT_A$Diet <- as.factor(d_DT_A$Diet)
d_DT_A$Batch <- as.factor(d_DT_A$Batch)
d_DT_A$Population_Lat <- factor(d_DT_A$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_DT_A$Population_Lon <- factor(d_DT_A$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_DT_A$Population_Alt <- factor(d_DT_A$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_DT_A$Line <- as.factor(d_DT_A$Line)
d_DT_A$Sex <- as.factor(d_DT_A$Sex)
d_DT_A$ReplicateVial <- as.factor(d_DT_A$ReplicateVial)
d_DT_A$DT_EggAdult <- as.numeric(d_DT_A$DT_EggAdult)
str(d_DT_A) #They should be ok now. 
## 'data.frame':    57609 obs. of  17 variables:
##  $ Supervisor.PI   : Factor w/ 6 levels "Gibert","Grath",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet            : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch           : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : Factor w/ 171 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex             : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : Factor w/ 2300 levels "Gibert_1_AK1_1",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Individual      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ DT_EggAdult     : num  202 202 202 202 202 202 202 202 202 202 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat  : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon  : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt  : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
#Create data sheets for females (d_DT_A_F) and males (d_DT_A_M), by sorting at sex level.
d_DT_A_F <-subset(d_DT_A,Sex=='F')
d_DT_A_M <-subset(d_DT_A,Sex=='M')

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview
min_DT_A <- min(d_DT_A$DT_EggAdult)
max_DT_A <- max(d_DT_A$DT_EggAdult)

y-axis is scaled by the minimum (150) and maximum (394) values in the full data set.

Gibert Lab
anova(DT_A_F_lmer_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 2839.4  354.93     8 157.74  3.7809 0.0004399 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Gibert)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Gibert", ]
## 
## REML criterion at convergence: 50925
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4211 -0.5914 -0.1334  0.4381  5.9468 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept)  4.727   2.174   
##  Line:Population    (Intercept) 36.110   6.009   
##  Batch              (Intercept)  1.175   1.084   
##  Residual                       93.874   9.689   
## Number of obs: 6817, groups:  
## ReplicateVial:Line, 531; Line:Population, 169; Batch, 3
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  222.3797     1.5710  19.3362 141.556   <2e-16 ***
## PopulationGI   4.3367     2.1688 156.6501   2.000   0.0473 *  
## PopulationKA   0.9782     2.0009 154.4950   0.489   0.6256    
## PopulationMA  -0.2899     2.0046 155.4376  -0.145   0.8852    
## PopulationMU   1.3078     2.0003 154.3141   0.654   0.5142    
## PopulationRE   5.4719     2.1366 158.5080   2.561   0.0114 *  
## PopulationUM   3.1059     2.0547 154.2259   1.512   0.1327    
## PopulationVA   1.5098     2.0056 155.9084   0.753   0.4527    
## PopulationYE  -4.5778     2.0223 160.6684  -2.264   0.0249 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.572                                                 
## PopulatinKA -0.620  0.461                                          
## PopulatinMA -0.619  0.460  0.500                                   
## PopulatinMU -0.621  0.461  0.501  0.500                            
## PopulatinRE -0.581  0.433  0.469  0.467  0.469                     
## PopulatinUM -0.604  0.449  0.488  0.487  0.488  0.456              
## PopulatinVA -0.619  0.460  0.500  0.499  0.500  0.467  0.487       
## PopulatinYE -0.614  0.456  0.496  0.495  0.496  0.463  0.483  0.494
anova(DT_A_M_lmer_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population 2526.4   315.8     8 155.05  2.9617 0.004119 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Gibert)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Gibert", ]
## 
## REML criterion at convergence: 49809.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4032 -0.6057 -0.1597  0.4187  5.0802 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept)   3.6928  1.9217 
##  Line:Population    (Intercept)  31.0297  5.5704 
##  Batch              (Intercept)   0.6211  0.7881 
##  Residual                       106.6292 10.3261 
## Number of obs: 6566, groups:  
## ReplicateVial:Line, 533; Line:Population, 169; Batch, 3
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  226.5155     1.4120  30.2571 160.427  < 2e-16 ***
## PopulationGI   5.3606     2.0277 153.6251   2.644  0.00905 ** 
## PopulationKA   0.7077     1.8726 152.1759   0.378  0.70601    
## PopulationMA  -1.2153     1.8789 154.0130  -0.647  0.51870    
## PopulationMU   1.2857     1.8708 151.6666   0.687  0.49300    
## PopulationRE   2.0681     1.9870 152.3834   1.041  0.29961    
## PopulationUM   3.2574     1.9227 151.8288   1.694  0.09227 .  
## PopulationVA   1.8677     1.8759 153.1712   0.996  0.32100    
## PopulationYE  -2.9951     1.8932 158.5817  -1.582  0.11564    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.600                                                 
## PopulatinKA -0.650  0.460                                          
## PopulatinMA -0.648  0.458  0.497                                   
## PopulatinMU -0.650  0.460  0.499  0.498                            
## PopulatinRE -0.612  0.434  0.469  0.468  0.470                     
## PopulatinUM -0.633  0.448  0.486  0.484  0.486  0.457              
## PopulatinVA -0.649  0.459  0.498  0.496  0.498  0.469  0.485       
## PopulatinYE -0.643  0.455  0.493  0.492  0.494  0.464  0.480  0.492

Grath Lab
anova(DT_A_F_lmer_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population 5055.6  2527.8     2 16.067  8.0465 0.003792 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Grath)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population)
##    Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Grath", ]
## 
## REML criterion at convergence: 7659.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0678 -0.7463  0.0894  0.5972  4.2158 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  22.4     4.732  
##  Residual                    314.1    17.724  
## Number of obs: 890, groups:  Line:Population, 23
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   309.626      1.722  13.465 179.800  < 2e-16 ***
## PopulationMU   -3.995      2.459  13.988  -1.624  0.12660    
## PopulationRE   11.990      3.989  18.136   3.006  0.00755 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltMU
## PopulatinMU -0.700       
## PopulatinRE -0.432  0.302
anova(DT_A_M_lmer_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 1999.4  999.71     2 18.482  3.3225 0.05852 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Grath)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population)
##    Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Grath", ]
## 
## REML criterion at convergence: 7557.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4732 -0.5678 -0.0897  0.6034  4.2694 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  47.61    6.90   
##  Residual                    300.89   17.35   
## Number of obs: 881, groups:  Line:Population, 23
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   317.567      2.349  17.582 135.217   <2e-16 ***
## PopulationMU   -5.420      3.330  17.762  -1.628    0.121    
## PopulationRE    6.730      4.998  19.040   1.347    0.194    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltMU
## PopulatinMU -0.705       
## PopulatinRE -0.470  0.331

Hoedjes Lab
anova(DT_A_F_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population   6899  862.38     8 155.01  5.6745 2.514e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Hoedjes", ]
## 
## REML criterion at convergence: 54971.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7059 -0.5552 -0.1930  0.3779 12.7479 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept)   4.685   2.164  
##  Line:Population    (Intercept)  21.642   4.652  
##  Batch              (Intercept)   1.381   1.175  
##  Residual                       151.975  12.328  
## Number of obs: 6939, groups:  
## ReplicateVial:Line, 501; Line:Population, 167; Batch, 4
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  231.2781     1.2896  30.0406 179.348  < 2e-16 ***
## PopulationGI   6.6655     1.7839 155.6038   3.737 0.000261 ***
## PopulationKA  -0.1798     1.6299 148.2949  -0.110 0.912318    
## PopulationMA   1.9330     1.6342 149.8397   1.183 0.238743    
## PopulationMU  -1.7771     1.6284 147.7808  -1.091 0.276922    
## PopulationRE   6.4500     1.7845 154.0927   3.614 0.000407 ***
## PopulationUM   0.9172     1.7105 151.0393   0.536 0.592592    
## PopulationVA  -1.3977     1.6366 150.5099  -0.854 0.394424    
## PopulationYE   1.8985     1.6491 154.8806   1.151 0.251422    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.573                                                 
## PopulatinKA -0.627  0.453                                          
## PopulatinMA -0.625  0.452  0.495                                   
## PopulatinMU -0.628  0.454  0.497  0.495                            
## PopulatinRE -0.573  0.420  0.453  0.452  0.454                     
## PopulatinUM -0.597  0.435  0.473  0.471  0.473  0.439              
## PopulatinVA -0.624  0.451  0.494  0.493  0.494  0.451  0.471       
## PopulatinYE -0.620  0.448  0.490  0.489  0.491  0.448  0.467  0.488
anova(DT_A_M_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 9369.8  1171.2     8 153.78  5.7073 2.332e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Hoedjes", ]
## 
## REML criterion at convergence: 54596.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.8700 -0.5946 -0.0822  0.3611 10.9394 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept)   3.107   1.763  
##  Line:Population    (Intercept)  24.219   4.921  
##  Batch              (Intercept)   2.565   1.602  
##  Residual                       205.215  14.325  
## Number of obs: 6649, groups:  
## ReplicateVial:Line, 501; Line:Population, 167; Batch, 4
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  238.6853     1.4661  19.7755 162.803  < 2e-16 ***
## PopulationGI   5.9614     1.8999 154.5025   3.138  0.00204 ** 
## PopulationKA  -2.5325     1.7402 149.2985  -1.455  0.14768    
## PopulationMA   0.6822     1.7356 147.7901   0.393  0.69486    
## PopulationMU  -2.1539     1.7370 148.2171  -1.240  0.21695    
## PopulationRE   5.7531     1.9095 155.4192   3.013  0.00302 ** 
## PopulationUM  -0.9229     1.8237 150.9070  -0.506  0.61354    
## PopulationVA  -2.8591     1.7426 149.9496  -1.641  0.10294    
## PopulationYE   0.5190     1.7673 157.2861   0.294  0.76941    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.542                                                 
## PopulatinKA -0.591  0.456                                          
## PopulatinMA -0.593  0.458  0.499                                   
## PopulatinMU -0.592  0.457  0.499  0.500                            
## PopulatinRE -0.539  0.423  0.454  0.455  0.455                     
## PopulatinUM -0.564  0.439  0.475  0.477  0.476  0.442              
## PopulatinVA -0.590  0.456  0.497  0.499  0.498  0.454  0.475       
## PopulatinYE -0.582  0.449  0.490  0.492  0.491  0.446  0.468  0.490

Schmidt Lab
anova(DT_A_F_lmer_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population   5630  703.75     8 145.04  3.2466 0.001981 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Schmidt)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population)
##    Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Schmidt", ]
## 
## REML criterion at convergence: 14067.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5685 -0.4672 -0.0841  0.3244  9.1996 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 382.1    19.55   
##  Residual                    216.8    14.72   
## Number of obs: 1664, groups:  Line:Population, 159
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   218.139      4.503 141.010  48.438  < 2e-16 ***
## PopulationGI    8.468      7.686 149.902   1.102   0.2724    
## PopulationKA    1.889      6.453 140.874   0.293   0.7702    
## PopulationMA    6.233      6.472 142.375   0.963   0.3371    
## PopulationMU    3.018      6.360 140.236   0.475   0.6358    
## PopulationRE   11.911      6.954 145.726   1.713   0.0889 .  
## PopulationUM    4.043      6.855 139.069   0.590   0.5563    
## PopulationVA   27.489      6.428 145.350   4.276 3.42e-05 ***
## PopulationYE    4.227      6.424 145.516   0.658   0.5116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.586                                                 
## PopulatinKA -0.698  0.409                                          
## PopulatinMA -0.696  0.408  0.486                                   
## PopulatinMU -0.708  0.415  0.494  0.493                            
## PopulatinRE -0.648  0.379  0.452  0.451  0.459                     
## PopulatinUM -0.657  0.385  0.459  0.457  0.465  0.425              
## PopulatinVA -0.701  0.410  0.489  0.487  0.496  0.454  0.460       
## PopulatinYE -0.701  0.411  0.489  0.488  0.496  0.454  0.461  0.491
anova(DT_A_M_lmer_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population 7928.7  991.09     8 141.78   3.337 0.001575 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Schmidt)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population)
##    Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Schmidt", ]
## 
## REML criterion at convergence: 12831.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8636 -0.4961 -0.1229  0.3065  7.0181 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 416.9    20.42   
##  Residual                    297.0    17.23   
## Number of obs: 1464, groups:  Line:Population, 157
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  226.01305    4.74292 135.96600  47.653  < 2e-16 ***
## PopulationGI  17.10410    8.12581 145.23581   2.105 0.037021 *  
## PopulationKA  -6.09966    6.80201 136.33867  -0.897 0.371436    
## PopulationMA   2.72810    6.84174 138.66550   0.399 0.690695    
## PopulationMU   0.70330    6.79893 136.35220   0.103 0.917763    
## PopulationRE   5.63782    7.36602 143.79829   0.765 0.445298    
## PopulationUM   3.09168    7.23539 135.25026   0.427 0.669840    
## PopulationVA  24.03542    6.84235 139.51268   3.513 0.000598 ***
## PopulationYE  -0.07554    6.79153 142.33291  -0.011 0.991141    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.584                                                 
## PopulatinKA -0.697  0.407                                          
## PopulatinMA -0.693  0.405  0.483                                   
## PopulatinMU -0.698  0.407  0.486  0.484                            
## PopulatinRE -0.644  0.376  0.449  0.446  0.449                     
## PopulatinUM -0.656  0.383  0.457  0.454  0.457  0.422              
## PopulatinVA -0.693  0.405  0.483  0.481  0.484  0.446  0.454       
## PopulatinYE -0.698  0.408  0.487  0.484  0.487  0.450  0.458  0.484

Stamenkovic-Radak Lab
anova(DT_A_F_lmer_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population  14440    1805     8 153.46  5.9074 1.365e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_StamenkovicRadak)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "StamenkovicRadak", ]
## 
## REML criterion at convergence: 54203.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5991 -0.5815 -0.1328  0.3761  6.5733 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept) 148.74   12.196  
##  Line:Population    (Intercept) 175.07   13.231  
##  Batch              (Intercept)  40.54    6.367  
##  Residual                       305.55   17.480  
## Number of obs: 6200, groups:  
## ReplicateVial:Line, 494; Line:Population, 165; Batch, 4
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  237.62349    4.73424  10.93403  50.193  2.8e-14 ***
## PopulationGI   7.46572    5.50910 152.34033   1.355 0.177372    
## PopulationKA  -8.47287    4.88982 151.57662  -1.733 0.085173 .  
## PopulationMA   8.62696    4.90527 153.46660   1.759 0.080619 .  
## PopulationMU  -0.06432    4.88517 151.27795  -0.013 0.989512    
## PopulationRE  17.72312    5.18652 152.11258   3.417 0.000812 ***
## PopulationUM  15.29379    5.03496 153.01586   3.038 0.002805 ** 
## PopulationVA  14.15780    4.96021 152.63502   2.854 0.004913 ** 
## PopulationYE   2.45664    4.91461 154.56893   0.500 0.617882    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.469                                                 
## PopulatinKA -0.530  0.454                                          
## PopulatinMA -0.526  0.452  0.510                                   
## PopulatinMU -0.530  0.454  0.513  0.511                            
## PopulatinRE -0.500  0.427  0.484  0.482  0.484                     
## PopulatinUM -0.515  0.440  0.499  0.495  0.498  0.471              
## PopulatinVA -0.521  0.450  0.504  0.504  0.505  0.475  0.489       
## PopulatinYE -0.528  0.452  0.511  0.507  0.510  0.482  0.496  0.502
anova(DT_A_M_lmer_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population  15603  1950.3     8 153.79  5.9197 1.315e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_StamenkovicRadak)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "StamenkovicRadak", ]
## 
## REML criterion at convergence: 51282.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9954 -0.5866 -0.1542  0.3744  6.6404 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept) 152.27   12.340  
##  Line:Population    (Intercept) 162.25   12.738  
##  Batch              (Intercept)  37.14    6.094  
##  Residual                       329.47   18.151  
## Number of obs: 5815, groups:  
## ReplicateVial:Line, 494; Line:Population, 165; Batch, 4
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   242.718      4.584  11.349  52.952    6e-15 ***
## PopulationGI    9.006      5.388 152.718   1.671 0.096703 .  
## PopulationKA   -7.893      4.781 151.809  -1.651 0.100849    
## PopulationMA    7.749      4.789 152.779   1.618 0.107700    
## PopulationMU    1.406      4.775 151.260   0.294 0.768857    
## PopulationRE   16.917      5.070 152.105   3.337 0.001065 ** 
## PopulationUM   14.365      4.933 154.211   2.912 0.004124 ** 
## PopulationVA   16.666      4.847 152.554   3.438 0.000755 ***
## PopulationYE    3.325      4.805 154.681   0.692 0.490005    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.473                                                 
## PopulatinKA -0.535  0.454                                          
## PopulatinMA -0.532  0.452  0.510                                   
## PopulatinMU -0.535  0.454  0.512  0.512                            
## PopulatinRE -0.505  0.426  0.484  0.482  0.484                     
## PopulatinUM -0.519  0.438  0.497  0.495  0.497  0.470              
## PopulatinVA -0.526  0.449  0.504  0.504  0.505  0.475  0.488       
## PopulatinYE -0.532  0.452  0.510  0.507  0.510  0.481  0.495  0.502

Zwaan Lab
anova(DT_A_F_lmer_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 1778.2  222.28     8 161.19  1.9922 0.05051 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Zwaan)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Zwaan", ]
## 
## REML criterion at convergence: 55980.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4229 -0.5259 -0.1848  0.2790  6.6947 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept)   8.246   2.872  
##  Line:Population    (Intercept)  25.504   5.050  
##  Batch              (Intercept)   6.708   2.590  
##  Residual                       111.578  10.563  
## Number of obs: 7330, groups:  
## ReplicateVial:Line, 521; Line:Population, 169; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  220.7481     2.2133   2.0039  99.735  9.9e-05 ***
## PopulationGI   4.1186     1.9094 158.6186   2.157  0.03251 *  
## PopulationKA   2.5714     1.7615 157.0943   1.460  0.14634    
## PopulationMA   3.4714     1.7605 157.1010   1.972  0.05038 .  
## PopulationMU   1.3777     1.7767 161.8964   0.775  0.43921    
## PopulationRE   5.5128     1.8672 156.8347   2.952  0.00364 ** 
## PopulationUM   1.6009     1.8048 155.7336   0.887  0.37641    
## PopulationVA   4.2001     1.7671 159.0337   2.377  0.01865 *  
## PopulationYE   0.5322     1.8015 167.9852   0.295  0.76803    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.365                                                 
## PopulatinKA -0.396  0.459                                          
## PopulatinMA -0.396  0.460  0.498                                   
## PopulatinMU -0.393  0.456  0.494  0.494                            
## PopulatinRE -0.374  0.436  0.470  0.471  0.466                     
## PopulatinUM -0.387  0.449  0.486  0.487  0.482  0.459              
## PopulatinVA -0.395  0.458  0.496  0.497  0.492  0.469  0.485       
## PopulatinYE -0.387  0.450  0.487  0.487  0.483  0.460  0.476  0.485
anova(DT_A_M_lmer_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 2206.9  275.87     8 160.63  2.2503 0.02641 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Zwaan)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Zwaan", ]
## 
## REML criterion at convergence: 49447.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9922 -0.5259 -0.1942  0.2460  6.2037 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept)   7.491   2.737  
##  Line:Population    (Intercept)  29.635   5.444  
##  Batch              (Intercept)   8.828   2.971  
##  Residual                       122.593  11.072  
## Number of obs: 6394, groups:  
## ReplicateVial:Line, 519; Line:Population, 169; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  225.7358     2.4877   1.8667  90.740 0.000204 ***
## PopulationGI   3.5977     2.0493 159.6267   1.756 0.081085 .  
## PopulationKA   1.2331     1.8806 154.7829   0.656 0.512985    
## PopulationMA   2.0196     1.8879 156.9895   1.070 0.286371    
## PopulationMU  -0.1906     1.8954 159.3747  -0.101 0.920023    
## PopulationRE   4.1796     2.0077 158.3623   2.082 0.038968 *  
## PopulationUM  -0.6705     1.9316 154.5842  -0.347 0.728972    
## PopulationVA   2.0535     1.8936 158.6298   1.084 0.279793    
## PopulationYE  -2.6216     1.9294 168.1729  -1.359 0.176034    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.348                                                 
## PopulatinKA -0.379  0.460                                          
## PopulatinMA -0.378  0.459  0.500                                   
## PopulatinMU -0.376  0.457  0.498  0.496                            
## PopulatinRE -0.355  0.434  0.470  0.469  0.467                     
## PopulatinUM -0.369  0.449  0.488  0.487  0.485  0.458              
## PopulatinVA -0.377  0.457  0.498  0.496  0.494  0.467  0.485       
## PopulatinYE -0.370  0.450  0.489  0.487  0.485  0.459  0.477  0.486

Lab correlations

There are 3 populations and 22 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Dry weight

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Colinet Lab : Sapho-Lou Marti , Hervé Colinet

Hoedjes Lab : Hristina Kostic, Katja Hoedjes

Onder Lab : Seda Coskun, Senel Selin Senkal, Dogus Can, Banu Sebnem Onder

Data Reformatting

Read data in R

d_DW <- read.csv("MasterSheets_Oct21_git/DW_MasterSheet_Oct21.csv")
str(d_DW)
## 'data.frame':    24161 obs. of  12 variables:
##  $ Supervisor.PI: chr  "Colinet" "Colinet" "Colinet" "Colinet" ...
##  $ Diet         : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch        : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population   : chr  "AK" "AK" "AK" "AK" ...
##  $ Line         : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Sex          : chr  "F" "F" "F" "F" ...
##  $ Individual   : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ DW_micrograms: num  0.394 0.473 0.581 0.594 0.454 0.486 0.581 0.584 0.596 0.573 ...
##  $ Country      : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude     : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude    : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude     : int  88 88 88 88 88 88 88 88 88 88 ...
d_DW$Supervisor.PI <- as.factor(d_DW$Supervisor.PI)
d_DW$Diet <- as.factor(d_DW$Diet)
d_DW$Batch <- as.factor(d_DW$Batch)
d_DW$Population <- as.factor(d_DW$Population)
d_DW$Population_Lat <- factor(d_DW$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_DW$Population_Lon <- factor(d_DW$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_DW$Population_Alt <- factor(d_DW$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_DW$Line <- as.factor(d_DW$Line)
d_DW$Sex <- as.factor(d_DW$Sex)
d_DW$DW_micrograms <- as.numeric(d_DW$DW_micrograms)
str(d_DW)
## 'data.frame':    24161 obs. of  15 variables:
##  $ Supervisor.PI : Factor w/ 3 levels "Colinet","Hoedjes",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet          : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch         : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population    : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Line          : Factor w/ 170 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex           : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Individual    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ DW_micrograms : num  0.394 0.473 0.581 0.594 0.454 0.486 0.581 0.584 0.596 0.573 ...
##  $ Country       : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude      : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude     : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude      : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat: Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon: Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt: Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_DW_F <-subset(d_DW,Sex=='F')
d_DW_M <-subset(d_DW,Sex=='M')

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_DW <- min(d_DW$DW_micrograms)
max_DW <- max(d_DW$DW_micrograms)

y-axis is scaled by the minimum (0.136) and maximum (0.73) values in the full data set.

Colinet Lab

anova(DW_F_lmer_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)  
## Population 0.0485 0.0060625     8 153.93  2.2495 0.0267 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DW_F_lmer_Colinet)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
##    Data: d_DW_F[d_DW_F$Supervisor.PI == "Colinet", ]
## 
## REML criterion at convergence: -11771.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2178 -0.6128  0.0273  0.6249  4.1344 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Population:Line (Intercept) 0.001328 0.03645 
##  Batch           (Intercept) 0.000219 0.01480 
##  Residual                    0.002695 0.05191 
## Number of obs: 3982, groups:  Population:Line, 166; Batch, 3
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    0.46881    0.01205   5.84387  38.893 2.79e-08 ***
## PopulationGI   0.01068    0.01336 157.01990   0.799 0.425384    
## PopulationKA   0.02967    0.01248 156.90419   2.378 0.018592 *  
## PopulationMA   0.03116    0.01216 155.02754   2.562 0.011353 *  
## PopulationMU   0.01577    0.01200 154.99934   1.314 0.190908    
## PopulationRE   0.04597    0.01336 156.97998   3.441 0.000742 ***
## PopulationUM   0.03221    0.01295 156.97058   2.488 0.013890 *  
## PopulationVA   0.02804    0.01248 156.90419   2.247 0.026005 *  
## PopulationYE   0.01217    0.01200 155.02544   1.014 0.312021    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.458                                                 
## PopulatinKA -0.490  0.497                                          
## PopulatinMA -0.492  0.448  0.480                                   
## PopulatinMU -0.498  0.449  0.481  0.493                            
## PopulatinRE -0.458  0.462  0.497  0.448  0.449                     
## PopulatinUM -0.472  0.477  0.515  0.462  0.464  0.478              
## PopulatinVA -0.490  0.497  0.537  0.480  0.481  0.497  0.515       
## PopulatinYE -0.498  0.449  0.481  0.493  0.500  0.449  0.464  0.481
anova(DW_M_lmer_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 0.027535 0.0034419     8 157.01  4.8067 2.632e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# could drop batch, as it explains nothing 
summary(DW_M_lmer_Colinet)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
##    Data: d_DW_M[d_DW_M$Supervisor.PI == "Colinet", ]
## 
## REML criterion at convergence: -17014.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6407 -0.5828 -0.0246  0.5790  7.4085 
## 
## Random effects:
##  Groups          Name        Variance  Std.Dev.
##  Population:Line (Intercept) 0.0003446 0.01856 
##  Batch           (Intercept) 0.0000000 0.00000 
##  Residual                    0.0007161 0.02676 
## Number of obs: 3975, groups:  Population:Line, 166; Batch, 3
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)   2.595e-01  4.327e-03  1.570e+02  59.966   <2e-16 ***
## PopulationGI  9.349e-04  6.612e-03  1.572e+02   0.141   0.8877    
## PopulationKA  1.965e-02  6.119e-03  1.570e+02   3.211   0.0016 ** 
## PopulationMA  1.168e-02  6.199e-03  1.570e+02   1.884   0.0614 .  
## PopulationMU  1.264e-02  6.119e-03  1.570e+02   2.066   0.0405 *  
## PopulationRE  1.658e-02  6.609e-03  1.570e+02   2.508   0.0131 *  
## PopulationUM  1.636e-02  6.383e-03  1.570e+02   2.563   0.0113 *  
## PopulationVA  8.075e-03  6.119e-03  1.570e+02   1.320   0.1889    
## PopulationYE -1.028e-02  6.119e-03  1.570e+02  -1.680   0.0949 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.654                                                 
## PopulatinKA -0.707  0.463                                          
## PopulatinMA -0.698  0.457  0.494                                   
## PopulatinMU -0.707  0.463  0.500  0.494                            
## PopulatinRE -0.655  0.428  0.463  0.457  0.463                     
## PopulatinUM -0.678  0.444  0.479  0.473  0.479  0.444              
## PopulatinVA -0.707  0.463  0.500  0.494  0.500  0.463  0.479       
## PopulatinYE -0.707  0.463  0.500  0.494  0.500  0.463  0.479  0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

Hoedjes Lab

anova(DW_F_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq   Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 0.0346 0.0043249     8 157.12  1.9203 0.06045 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DW_F_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
##    Data: d_DW_F[d_DW_F$Supervisor.PI == "Hoedjes", ]
## 
## REML criterion at convergence: -12532.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6857 -0.6210 -0.0043  0.6319  3.4401 
## 
## Random effects:
##  Groups          Name        Variance  Std.Dev.
##  Population:Line (Intercept) 1.272e-03 0.035659
##  Batch           (Intercept) 8.069e-05 0.008983
##  Residual                    2.252e-03 0.047457
## Number of obs: 4004, groups:  Population:Line, 167; Batch, 4
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  4.374e-01  9.404e-03 3.422e+01  46.507  < 2e-16 ***
## PopulationGI 1.093e-02  1.264e-02 1.572e+02   0.865  0.38857    
## PopulationKA 2.145e-02  1.169e-02 1.567e+02   1.835  0.06837 .  
## PopulationMA 5.763e-03  1.169e-02 1.567e+02   0.493  0.62262    
## PopulationMU 1.938e-02  1.168e-02 1.566e+02   1.658  0.09923 .  
## PopulationRE 2.810e-02  1.264e-02 1.572e+02   2.223  0.02761 *  
## PopulationUM 1.259e-02  1.220e-02 1.569e+02   1.032  0.30359    
## PopulationVA 3.573e-02  1.169e-02 1.568e+02   3.057  0.00263 ** 
## PopulationYE 4.792e-03  1.170e-02 1.574e+02   0.409  0.68276    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.574                                                 
## PopulatinKA -0.621  0.462                                          
## PopulatinMA -0.621  0.462  0.500                                   
## PopulatinMU -0.621  0.462  0.500  0.500                            
## PopulatinRE -0.574  0.428  0.462  0.462  0.462                     
## PopulatinUM -0.595  0.443  0.479  0.479  0.479  0.445              
## PopulatinVA -0.621  0.462  0.500  0.500  0.500  0.462  0.479       
## PopulatinYE -0.620  0.461  0.500  0.499  0.500  0.460  0.477  0.499
anova(DW_M_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Population 0.0033803 0.00042254     8 155.21  0.6263 0.7548
summary(DW_M_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
##    Data: d_DW_M[d_DW_M$Supervisor.PI == "Hoedjes", ]
## 
## REML criterion at convergence: -17309.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1189 -0.6261 -0.0298  0.6056  5.1645 
## 
## Random effects:
##  Groups          Name        Variance  Std.Dev.
##  Population:Line (Intercept) 3.883e-04 0.019704
##  Batch           (Intercept) 9.299e-05 0.009643
##  Residual                    6.746e-04 0.025974
## Number of obs: 3997, groups:  Population:Line, 167; Batch, 5
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  2.637e-01  6.342e-03 1.453e+01  41.576   <2e-16 ***
## PopulationGI 2.232e-03  6.987e-03 1.552e+02   0.319   0.7499    
## PopulationKA 1.077e-02  6.455e-03 1.549e+02   1.669   0.0972 .  
## PopulationMA 3.160e-03  6.456e-03 1.550e+02   0.490   0.6252    
## PopulationMU 5.546e-03  6.457e-03 1.550e+02   0.859   0.3917    
## PopulationRE 6.302e-03  6.993e-03 1.560e+02   0.901   0.3688    
## PopulationUM 1.735e-03  6.739e-03 1.552e+02   0.257   0.7972    
## PopulationVA 8.651e-03  6.460e-03 1.553e+02   1.339   0.1825    
## PopulationYE 1.040e-03  6.460e-03 1.551e+02   0.161   0.8723    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.474                                                 
## PopulatinKA -0.509  0.463                                          
## PopulatinMA -0.511  0.463  0.500                                   
## PopulatinMU -0.509  0.462  0.500  0.500                            
## PopulatinRE -0.470  0.429  0.463  0.463  0.462                     
## PopulatinUM -0.487  0.444  0.479  0.480  0.479  0.444              
## PopulatinVA -0.513  0.463  0.500  0.500  0.500  0.463  0.479       
## PopulatinYE -0.511  0.463  0.500  0.500  0.500  0.462  0.478  0.500

Onder Lab

anova(DW_F_lmer_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 0.11338 0.014173     8 150.87  5.3596 6.168e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DW_F_lmer_Onder)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
##    Data: d_DW_F[d_DW_F$Supervisor.PI == "Onder", ]
## 
## REML criterion at convergence: -12234.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.7744 -0.5882  0.0568  0.6380  3.6377 
## 
## Random effects:
##  Groups          Name        Variance  Std.Dev.
##  Population:Line (Intercept) 0.0011087 0.03330 
##  Batch           (Intercept) 0.0002207 0.01485 
##  Residual                    0.0026444 0.05142 
## Number of obs: 4102, groups:  Population:Line, 166; Batch, 5
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  4.254e-01  1.032e-02 1.779e+01  41.206  < 2e-16 ***
## PopulationGI 1.338e-02  1.205e-02 1.521e+02   1.110 0.268763    
## PopulationKA 3.495e-02  1.123e-02 1.540e+02   3.113 0.002211 ** 
## PopulationMA 4.429e-02  1.104e-02 1.494e+02   4.012 9.50e-05 ***
## PopulationMU 1.512e-02  1.104e-02 1.491e+02   1.369 0.172928    
## PopulationRE 4.440e-02  1.183e-02 1.519e+02   3.754 0.000247 ***
## PopulationUM 3.807e-02  1.170e-02 1.542e+02   3.254 0.001398 ** 
## PopulationVA 4.794e-02  1.106e-02 1.499e+02   4.333 2.68e-05 ***
## PopulationYE 3.795e-03  1.136e-02 1.497e+02   0.334 0.738687    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.500                                                 
## PopulatinKA -0.543  0.474                                          
## PopulatinMA -0.535  0.458  0.492                                   
## PopulatinMU -0.539  0.462  0.498  0.500                            
## PopulatinRE -0.508  0.446  0.483  0.467  0.471                     
## PopulatinUM -0.523  0.451  0.494  0.472  0.479  0.461              
## PopulatinVA -0.542  0.465  0.502  0.499  0.502  0.472  0.482       
## PopulatinYE -0.523  0.448  0.483  0.486  0.488  0.458  0.465  0.487
anova(DW_M_lmer_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq  Mean Sq NumDF DenDF F value  Pr(>F)  
## Population 0.014208 0.001776     8 147.4  1.9511 0.05659 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DW_M_lmer_Onder)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
##    Data: d_DW_M[d_DW_M$Supervisor.PI == "Onder", ]
## 
## REML criterion at convergence: -16576.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2689 -0.6120 -0.0072  0.6481  4.0118 
## 
## Random effects:
##  Groups          Name        Variance  Std.Dev.
##  Population:Line (Intercept) 4.359e-04 0.020877
##  Batch           (Intercept) 9.033e-05 0.009504
##  Residual                    9.103e-04 0.030171
## Number of obs: 4101, groups:  Population:Line, 166; Batch, 5
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    0.255121   0.006505  16.364942  39.220   <2e-16 ***
## PopulationGI  -0.003951   0.007512 148.441698  -0.526   0.5997    
## PopulationKA   0.007906   0.006996 150.347382   1.130   0.2602    
## PopulationMA   0.016584   0.006883 146.065835   2.409   0.0172 *  
## PopulationMU   0.006807   0.006883 145.768741   0.989   0.3243    
## PopulationRE   0.009746   0.007372 148.371553   1.322   0.1882    
## PopulationUM   0.012761   0.007291 150.560217   1.750   0.0821 .  
## PopulationVA   0.011227   0.006898 146.525460   1.628   0.1057    
## PopulationYE  -0.002872   0.007079 146.340028  -0.406   0.6855    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.494                                                 
## PopulatinKA -0.537  0.474                                          
## PopulatinMA -0.529  0.458  0.492                                   
## PopulatinMU -0.533  0.462  0.498  0.500                            
## PopulatinRE -0.503  0.446  0.482  0.467  0.471                     
## PopulatinUM -0.517  0.450  0.493  0.472  0.479  0.460              
## PopulatinVA -0.536  0.465  0.502  0.499  0.501  0.472  0.482       
## PopulatinYE -0.518  0.448  0.483  0.486  0.488  0.458  0.465  0.487

Lab correlations

There are 161 populations and 9 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Thorax length

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Kozeretska Lab : Svitlana Serga, Alexandra Protsenko, Oleksandr Maistrenko, Iryna Kozeretska

Posnien Lab : Micael Reis, Lennart Hüper

Ritchie Lab : Megan Mcgunnigle, Nicola Cook, Teresa Abaurrea, Michael Ritchie

Schmidt Lab : Amy Goldfischer, Paul Schmidt

Data Reformatting

Read data in R

d_TL <- read.csv("MasterSheets_Oct21_git/TL_MasterSheet_Feb22.csv")
str(d_TL)
## 'data.frame':    13754 obs. of  14 variables:
##  $ Supervisor.PI   : chr  "Ritchie" "Ritchie" "Ritchie" "Ritchie" ...
##  $ Diet            : chr  "S" "S" "S" "S" ...
##  $ Batch           : int  15 15 1 1 1 2 1 1 1 1 ...
##  $ Population      : chr  "GI" "GI" "KA" "KA" ...
##  $ Line            : chr  "GI5" "GI5" "KA1" "KA1" ...
##  $ Sex             : chr  "M" "M" "M" "M" ...
##  $ ReplicateVialOld: int  3 1 3 2 2 2 2 2 2 2 ...
##  $ ReplicateVial   : chr  "Ritchie_15_GI5_3" "Ritchie_15_GI5_1" "Ritchie_1_KA1_3" "Ritchie_1_KA1_2" ...
##  $ Individual      : int  1117 1060 1210 1170 1169 51 1182 1173 1172 1171 ...
##  $ TL_micrometers  : num  300 340 460 514 515 521 552 558 565 580 ...
##  $ Country         : chr  "Spain" "Spain" "Denmark" "Denmark" ...
##  $ Latitude        : num  41.6 41.6 55.9 55.9 55.9 ...
##  $ Longitude       : num  0.62 0.62 10.21 10.21 10.21 ...
##  $ Altitude        : int  173 173 15 15 15 173 15 15 15 15 ...
str(d_TL)
## 'data.frame':    13754 obs. of  14 variables:
##  $ Supervisor.PI   : chr  "Ritchie" "Ritchie" "Ritchie" "Ritchie" ...
##  $ Diet            : chr  "S" "S" "S" "S" ...
##  $ Batch           : int  15 15 1 1 1 2 1 1 1 1 ...
##  $ Population      : chr  "GI" "GI" "KA" "KA" ...
##  $ Line            : chr  "GI5" "GI5" "KA1" "KA1" ...
##  $ Sex             : chr  "M" "M" "M" "M" ...
##  $ ReplicateVialOld: int  3 1 3 2 2 2 2 2 2 2 ...
##  $ ReplicateVial   : chr  "Ritchie_15_GI5_3" "Ritchie_15_GI5_1" "Ritchie_1_KA1_3" "Ritchie_1_KA1_2" ...
##  $ Individual      : int  1117 1060 1210 1170 1169 51 1182 1173 1172 1171 ...
##  $ TL_micrometers  : num  300 340 460 514 515 521 552 558 565 580 ...
##  $ Country         : chr  "Spain" "Spain" "Denmark" "Denmark" ...
##  $ Latitude        : num  41.6 41.6 55.9 55.9 55.9 ...
##  $ Longitude       : num  0.62 0.62 10.21 10.21 10.21 ...
##  $ Altitude        : int  173 173 15 15 15 173 15 15 15 15 ...
d_TL$Supervisor.PI <- as.factor(d_TL$Supervisor.PI)
d_TL$Diet <- as.factor(d_TL$Diet)
d_TL$Batch <- as.factor(d_TL$Batch)
d_TL$Population_Lat <- factor(d_TL$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_TL$Population_Lon <- factor(d_TL$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_TL$Population_Alt <- factor(d_TL$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_TL$Line <- as.factor(d_TL$Line)
d_TL$Sex <- as.factor(d_TL$Sex)
d_TL$ReplicateVial <- as.factor(d_TL$ReplicateVial)
d_TL$TL_micrometers <- as.numeric(d_TL$TL_micrometers)
str(d_TL)
## 'data.frame':    13754 obs. of  17 variables:
##  $ Supervisor.PI   : Factor w/ 4 levels "Kozeretska","Posnien",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ Diet            : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Batch           : Factor w/ 12 levels "1","2","3","4",..: 12 12 1 1 1 2 1 1 1 1 ...
##  $ Population      : chr  "GI" "GI" "KA" "KA" ...
##  $ Line            : Factor w/ 166 levels "AK1","AK10","AK11",..: 31 31 36 36 36 21 36 36 36 36 ...
##  $ Sex             : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
##  $ ReplicateVialOld: int  3 1 3 2 2 2 2 2 2 2 ...
##  $ ReplicateVial   : Factor w/ 503 levels "Kozeretska_1_AK1_1",..: 298 296 271 270 270 306 270 270 270 270 ...
##  $ Individual      : int  1117 1060 1210 1170 1169 51 1182 1173 1172 1171 ...
##  $ TL_micrometers  : num  300 340 460 514 515 521 552 558 565 580 ...
##  $ Country         : chr  "Spain" "Spain" "Denmark" "Denmark" ...
##  $ Latitude        : num  41.6 41.6 55.9 55.9 55.9 ...
##  $ Longitude       : num  0.62 0.62 10.21 10.21 10.21 ...
##  $ Altitude        : int  173 173 15 15 15 173 15 15 15 15 ...
##  $ Population_Lat  : Factor w/ 9 levels "YE","RE","GI",..: 3 3 7 7 7 3 7 7 7 7 ...
##  $ Population_Lon  : Factor w/ 9 levels "RE","GI","KA",..: 2 2 3 3 3 2 3 3 3 3 ...
##  $ Population_Alt  : Factor w/ 9 levels "KA","AK","GI",..: 3 3 1 1 1 3 1 1 1 1 ...
d_TL_F <-subset(d_TL,Sex=='F')
d_TL_M <-subset(d_TL,Sex=='M')

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_TL <- min(d_TL$TL_micrometers)
max_TL <- max(d_TL$TL_micrometers)

y-axis is scaled by the minimum (300) and maximum (1232) values in the full data set.

Kozeretska Lab

anova(TL_F_lmer_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population  46443  5805.4     8 119.33  2.7643 0.007735 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(TL_F_lmer_Kozeretska)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_TL_F, Supervisor.PI == "Kozeretska"))
## 
## REML criterion at convergence: 51045.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4816 -0.6029  0.0635  0.6779  3.5476 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  948.80  30.803  
##  Line:Population               (Intercept)  768.84  27.728  
##  Batch                         (Intercept)   16.12   4.015  
##  Residual                                  2100.14  45.827  
## Number of obs: 4810, groups:  
## ReplicateVial:Line:Population, 241; Line:Population, 130; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   936.544      8.986  32.423 104.222   <2e-16 ***
## PopulationGI   -1.508     15.049 123.353  -0.100   0.9204    
## PopulationKA  -24.921     13.441 116.126  -1.854   0.0663 .  
## PopulationMA   27.752     12.426 123.675   2.233   0.0273 *  
## PopulationMU    6.347     12.310 120.143   0.516   0.6071    
## PopulationRE   25.851     13.643 121.381   1.895   0.0605 .  
## PopulationUM    4.918     13.538 118.603   0.363   0.7171    
## PopulationVA   21.821     11.915 118.745   1.831   0.0695 .  
## PopulationYE   13.870     13.395 123.600   1.036   0.3024    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.537                                                 
## PopulatinKA -0.602  0.359                                          
## PopulatinMA -0.650  0.388  0.435                                   
## PopulatinMU -0.657  0.392  0.439  0.475                            
## PopulatinRE -0.593  0.354  0.396  0.428  0.433                     
## PopulatinUM -0.597  0.356  0.399  0.432  0.436  0.393              
## PopulatinVA -0.679  0.405  0.454  0.490  0.495  0.447  0.450       
## PopulatinYE -0.603  0.360  0.403  0.436  0.440  0.397  0.400  0.455
anova(TL_M_lmer_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population  43426  5428.3     8 120.17  2.9842 0.004377 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(TL_M_lmer_Kozeretska)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population) + (1 | Batch) +  
##     (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_TL_M, Supervisor.PI == "Kozeretska"))
## 
## REML criterion at convergence: 50338.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8281 -0.5822  0.0434  0.6572  7.9545 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  902.23  30.037  
##  Line:Population               (Intercept)  672.48  25.932  
##  Batch                         (Intercept)   11.11   3.333  
##  Residual                                  1819.03  42.650  
## Number of obs: 4807, groups:  
## ReplicateVial:Line:Population, 241; Line:Population, 130; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   835.238      8.442  38.635  98.940   <2e-16 ***
## PopulationGI   -4.009     14.310 124.262  -0.280   0.7798    
## PopulationKA  -22.734     12.772 116.890  -1.780   0.0777 .  
## PopulationMA   27.562     11.816 124.613   2.333   0.0213 *  
## PopulationMU    2.935     11.702 120.984   0.251   0.8024    
## PopulationRE   24.606     12.970 122.241   1.897   0.0602 .  
## PopulationUM    3.492     12.868 119.413   0.271   0.7866    
## PopulationVA   23.225     11.325 119.573   2.051   0.0425 *  
## PopulationYE   10.507     12.737 124.515   0.825   0.4110    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.543                                                 
## PopulatinKA -0.609  0.359                                          
## PopulatinMA -0.658  0.388  0.435                                   
## PopulatinMU -0.665  0.392  0.439  0.475                            
## PopulatinRE -0.600  0.354  0.397  0.428  0.433                     
## PopulatinUM -0.605  0.357  0.400  0.432  0.436  0.394              
## PopulatinVA -0.687  0.405  0.454  0.491  0.496  0.447  0.451       
## PopulatinYE -0.611  0.360  0.404  0.436  0.440  0.397  0.401  0.455

Posnien Lab

anova(TL_F_lmer_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population  24414  3051.8     8    18  1.9851 0.1082
summary(TL_F_lmer_Posnien)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## TL_micrometers ~ Population + (1 | Line:Population) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_TL_F, Supervisor.PI == "Posnien"))
## 
## REML criterion at convergence: 2722.8
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.86894 -0.60033  0.05782  0.63035  2.78455 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  Line:Population               (Intercept)  463.9   21.54   
##  ReplicateVial:Line:Population (Intercept)  542.6   23.29   
##  Residual                                  1537.3   39.21   
## Number of obs: 270, groups:  
## Line:Population, 27; ReplicateVial:Line:Population, 27
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   880.664     19.666  18.000  44.782   <2e-16 ***
## PopulationGI   63.713     27.811  18.000   2.291   0.0343 *  
## PopulationKA   47.941     27.811  18.000   1.724   0.1019    
## PopulationMA    1.333     27.811  18.000   0.048   0.9623    
## PopulationMU  -14.433     27.811  18.000  -0.519   0.6101    
## PopulationRE   22.108     27.811  18.000   0.795   0.4370    
## PopulationUM   27.766     27.811  18.000   0.998   0.3313    
## PopulationVA   17.765     27.811  18.000   0.639   0.5310    
## PopulationYE   61.181     27.811  18.000   2.200   0.0411 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707                                                 
## PopulatinKA -0.707  0.500                                          
## PopulatinMA -0.707  0.500  0.500                                   
## PopulatinMU -0.707  0.500  0.500  0.500                            
## PopulatinRE -0.707  0.500  0.500  0.500  0.500                     
## PopulatinUM -0.707  0.500  0.500  0.500  0.500  0.500              
## PopulatinVA -0.707  0.500  0.500  0.500  0.500  0.500  0.500       
## PopulatinYE -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500
anova(TL_M_lmer_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
## Population  22105  2763.1     8    18   2.274 0.07047 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(TL_M_lmer_Posnien)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population)
##    Data: (subset(d_TL_M, Supervisor.PI == "Posnien"))
## 
## REML criterion at convergence: 2661.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7213 -0.5390 -0.0356  0.5975  3.7202 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  814.9   28.55   
##  Residual                    1215.1   34.86   
## Number of obs: 270, groups:  Line:Population, 27
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   777.155     17.667  18.000  43.988   <2e-16 ***
## PopulationGI   64.073     24.986  18.000   2.564   0.0195 *  
## PopulationKA   46.404     24.986  18.000   1.857   0.0797 .  
## PopulationMA    8.276     24.986  18.000   0.331   0.7443    
## PopulationMU  -20.491     24.986  18.000  -0.820   0.4229    
## PopulationRE   25.437     24.986  18.000   1.018   0.3221    
## PopulationUM   44.733     24.986  18.000   1.790   0.0902 .  
## PopulationVA   19.513     24.986  18.000   0.781   0.4450    
## PopulationYE   42.405     24.986  18.000   1.697   0.1069    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707                                                 
## PopulatinKA -0.707  0.500                                          
## PopulatinMA -0.707  0.500  0.500                                   
## PopulatinMU -0.707  0.500  0.500  0.500                            
## PopulatinRE -0.707  0.500  0.500  0.500  0.500                     
## PopulatinUM -0.707  0.500  0.500  0.500  0.500  0.500              
## PopulatinVA -0.707  0.500  0.500  0.500  0.500  0.500  0.500       
## PopulatinYE -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500

Ritchie Lab

anova(TL_F_lmer_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population  10416  2083.2     5 12.944  0.7397 0.6073
summary(TL_F_lmer_Ritchie)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: (subset(d_TL_F, Supervisor.PI == "Ritchie"))
## 
## REML criterion at convergence: 11444.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.7332 -0.5260  0.0394  0.6188  5.1914 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  776.8   27.87   
##  Batch           (Intercept)  913.4   30.22   
##  Residual                    2816.3   53.07   
## Number of obs: 1059, groups:  Line:Population, 26; Batch, 12
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   955.312     19.715  19.553  48.456   <2e-16 ***
## PopulationGI   14.119     19.934  10.225   0.708    0.495    
## PopulationKA    1.041     26.849  19.182   0.039    0.969    
## PopulationMA   21.660     26.819  19.097   0.808    0.429    
## PopulationMU   15.464     31.445  17.499   0.492    0.629    
## PopulationYE   33.399     22.153  13.622   1.508    0.154    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU
## PopulatinGI -0.584                            
## PopulatinKA -0.727  0.430                     
## PopulatinMA -0.728  0.430  0.765              
## PopulatinMU -0.584  0.370  0.537  0.538       
## PopulatinYE -0.651  0.538  0.491  0.492  0.484
anova(TL_M_lmer_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population  11596  2319.3     5 12.762   0.648 0.6682
summary(TL_M_lmer_Ritchie)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: (subset(d_TL_M, Supervisor.PI == "Ritchie"))
## 
## REML criterion at convergence: 11309.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -8.8016 -0.5065  0.0525  0.5479  3.8390 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  824.6   28.72   
##  Batch           (Intercept) 1396.0   37.36   
##  Residual                    3579.3   59.83   
## Number of obs: 1024, groups:  Line:Population, 26; Batch, 12
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   826.886     22.014  19.340  37.561   <2e-16 ***
## PopulationGI   -3.848     20.744  10.402  -0.185    0.856    
## PopulationKA  -11.823     30.050  19.192  -0.393    0.698    
## PopulationMA   13.633     30.055  19.206   0.454    0.655    
## PopulationMU   17.666     34.006  17.335   0.519    0.610    
## PopulationYE   19.335     23.473  13.320   0.824    0.425    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU
## PopulatinGI -0.547                            
## PopulatinKA -0.719  0.402                     
## PopulatinMA -0.718  0.402  0.797              
## PopulatinMU -0.582  0.359  0.560  0.560       
## PopulatinYE -0.625  0.534  0.476  0.476  0.491

Schmidt Lab

anova(TL_F_lmer_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)    
## Population  38709  4838.7     8 146.7   3.837 0.000399 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(TL_F_lmer_Schmidt)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population)
##    Data: (subset(d_TL_F, Supervisor.PI == "Schmidt"))
## 
## REML criterion at convergence: 15278.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -8.8431 -0.5418  0.0956  0.6004  2.7028 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  436.8   20.90   
##  Residual                    1261.1   35.51   
## Number of obs: 1514, groups:  Line:Population, 157
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   995.047      5.305 143.475 187.553  < 2e-16 ***
## PopulationGI   26.903      8.516 147.382   3.159  0.00192 ** 
## PopulationKA   10.337      7.599 143.310   1.360  0.17587    
## PopulationMA   19.845      7.632 145.365   2.600  0.01028 *  
## PopulationMU    9.724      7.512 144.073   1.295  0.19756    
## PopulationRE   41.671      8.349 148.572   4.991 1.66e-06 ***
## PopulationUM   16.035      8.124 144.736   1.974  0.05032 .  
## PopulationVA   17.287      7.834 143.937   2.207  0.02891 *  
## PopulationYE   14.839      7.547 146.651   1.966  0.05116 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.623                                                 
## PopulatinKA -0.698  0.435                                          
## PopulatinMA -0.695  0.433  0.485                                   
## PopulatinMU -0.706  0.440  0.493  0.491                            
## PopulatinRE -0.635  0.396  0.444  0.442  0.449                     
## PopulatinUM -0.653  0.407  0.456  0.454  0.461  0.415              
## PopulatinVA -0.677  0.422  0.473  0.471  0.478  0.430  0.442       
## PopulatinYE -0.703  0.438  0.491  0.489  0.497  0.447  0.459  0.476

Lab correlations

There are 4 populations and 6 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

pdf(file="ThoraxLength/p_TL_F_lab_cor.pdf",width=8, height=8)
p_TL_F_lab_cor()
invisible(dev.off())

pdf(file="ThoraxLength/p_TL_M_lab_cor.pdf",width=8, height=8)
p_TL_M_lab_cor()
invisible(dev.off())

Wing area

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Onder Lab : Cansu Aksoy, Ekin Demir, Ezgi Cobanoglu, Banu Sebnem Onder

Posnien Lab : Micael Reis, Lennart Hüper, Nico Posnien

Ritchie Lab : Megan Mcgunnigle, Nicola Cook, Teresa Abaurrea, Marija Tanaskovic, Michael Ritchie

Stamenkovic-Radak Lab : Marija Savic Veselinovic, Marija Tanaskovic, Aleksandra Patenkovic, Filip Filopovski, Mihailo Jelic, Katarina Eric, Pavle Eric, Slobodan Davidovic, Marina Stamenkovic-Radak

Data Reformatting

d_WA <- read.csv("MasterSheets_Oct21_git/WA_MasterSheet_Nov21.csv")
str(d_WA)
## 'data.frame':    21201 obs. of  15 variables:
##  $ Supervisor.PI                : chr  "StamenkovicRadak" "StamenkovicRadak" "StamenkovicRadak" "StamenkovicRadak" ...
##  $ Diet                         : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch                        : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population                   : chr  "YE" "YE" "YE" "YE" ...
##  $ Line                         : chr  "YE13" "YE13" "YE13" "YE13" ...
##  $ Sex                          : chr  "F" "F" "F" "F" ...
##  $ ReplicateVialOld             : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial                : chr  "StamenkovicRadak_1_YE13_1" "StamenkovicRadak_1_YE13_1" "StamenkovicRadak_1_YE13_1" "StamenkovicRadak_1_YE13_1" ...
##  $ Individual                   : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ CentroidSizeLeft_micrometers : num  2869 2910 2901 3050 2906 ...
##  $ CentroidSizeRight_micrometers: num  2891 2889 2881 3039 2919 ...
##  $ Country                      : chr  "Turkey" "Turkey" "Turkey" "Turkey" ...
##  $ Latitude                     : num  40.2 40.2 40.2 40.2 40.2 ...
##  $ Longitude                    : num  32.3 32.3 32.3 32.3 32.3 ...
##  $ Altitude                     : int  680 680 680 680 680 680 680 680 680 680 ...
d_WA$Supervisor.PI <- as.factor(d_WA$Supervisor.PI)
d_WA$Diet <- as.factor(d_WA$Diet)
d_WA$Batch <- as.factor(d_WA$Batch)
d_WA$Population_Lat <- factor(d_WA$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_WA$Population_Lon <- factor(d_WA$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_WA$Population_Alt <- factor(d_WA$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_WA$Line <- as.factor(d_WA$Line)
d_WA$Sex <- as.factor(d_WA$Sex)
d_WA$ReplicateVial <- as.factor(d_WA$ReplicateVial)
d_WA$CentroidSizeLeft_micrometers <- as.numeric(d_WA$CentroidSizeLeft_micrometers)
d_WA$CentroidSizeRight_micrometers <- as.numeric(d_WA$CentroidSizeRight_micrometers)
str(d_WA)
## 'data.frame':    21201 obs. of  18 variables:
##  $ Supervisor.PI                : Factor w/ 4 levels "Onder","Posnien",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ Diet                         : Factor w/ 2 levels "NS","S": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch                        : Factor w/ 15 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population                   : chr  "YE" "YE" "YE" "YE" ...
##  $ Line                         : Factor w/ 172 levels "AK1","AK10","AK11",..: 154 154 154 154 154 154 154 154 154 154 ...
##  $ Sex                          : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVialOld             : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial                : Factor w/ 806 levels "Onder_1_AK1_1",..: 711 711 711 711 711 711 711 711 711 711 ...
##  $ Individual                   : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ CentroidSizeLeft_micrometers : num  2869 2910 2901 3050 2906 ...
##  $ CentroidSizeRight_micrometers: num  2891 2889 2881 3039 2919 ...
##  $ Country                      : chr  "Turkey" "Turkey" "Turkey" "Turkey" ...
##  $ Latitude                     : num  40.2 40.2 40.2 40.2 40.2 ...
##  $ Longitude                    : num  32.3 32.3 32.3 32.3 32.3 ...
##  $ Altitude                     : int  680 680 680 680 680 680 680 680 680 680 ...
##  $ Population_Lat               : Factor w/ 9 levels "YE","RE","GI",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population_Lon               : Factor w/ 9 levels "RE","GI","KA",..: 8 8 8 8 8 8 8 8 8 8 ...
##  $ Population_Alt               : Factor w/ 9 levels "KA","AK","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
d_WA_F <-subset(d_WA,Sex=='F')
d_WA_M <-subset(d_WA,Sex=='M')

Left

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_WAL <- min(d_WA$CentroidSizeLeft_micrometers)
max_WAL <- max(d_WA$CentroidSizeLeft_micrometers)

y-axis is scaled by the minimum (1857.612491) and maximum (3333.0502) values in the full data set.

Onder Lab

anova(WA_F_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 416463   52058     8 151.87  8.9484 5.178e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_L_Onder_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: d_WA_F %>% filter(Supervisor.PI == "Onder")
## 
## REML criterion at convergence: 61105.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7289 -0.5621  0.0553  0.6205  4.0410 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  781.8   27.96   
##  Line:Population               (Intercept) 3090.7   55.59   
##  Batch                         (Intercept) 1209.3   34.77   
##  Residual                                  5817.5   76.27   
## Number of obs: 5247, groups:  
## ReplicateVial:Line:Population, 518; Line:Population, 167; Batch, 7
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  2813.825     19.103   14.957 147.300  < 2e-16 ***
## PopulationGI   39.068     20.578  152.357   1.899   0.0595 .  
## PopulationKA   44.852     19.374  155.067   2.315   0.0219 *  
## PopulationMA   84.984     18.845  150.624   4.510 1.30e-05 ***
## PopulationMU   46.278     18.935  151.042   2.444   0.0157 *  
## PopulationRE  130.045     20.225  152.488   6.430 1.55e-09 ***
## PopulationUM   87.275     19.956  153.548   4.373 2.25e-05 ***
## PopulationVA   29.681     18.989  151.898   1.563   0.1201    
## PopulationYE    1.476     19.176  151.285   0.077   0.9387    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.459                                                 
## PopulatinKA -0.505  0.469                                          
## PopulatinMA -0.488  0.457  0.485                                   
## PopulatinMU -0.506  0.460  0.503  0.497                            
## PopulatinRE -0.473  0.441  0.484  0.463  0.476                     
## PopulatinUM -0.487  0.449  0.497  0.468  0.483  0.464              
## PopulatinVA -0.499  0.465  0.511  0.497  0.505  0.477  0.486       
## PopulatinYE -0.495  0.459  0.501  0.490  0.498  0.472  0.480  0.499
anova(WA_M_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 294608   36826     8 155.79  7.8374 7.989e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_M_L_Onder_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_M, Supervisor.PI == "Onder"))
## 
## REML criterion at convergence: 59806.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2993 -0.5881  0.0332  0.6103  5.8833 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  765.6   27.67   
##  Line:Population               (Intercept) 2085.2   45.66   
##  Batch                         (Intercept)  336.5   18.34   
##  Residual                                  4698.8   68.55   
## Number of obs: 5230, groups:  
## ReplicateVial:Line:Population, 518; Line:Population, 167; Batch, 7
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  2444.130     13.489   29.295 181.192  < 2e-16 ***
## PopulationGI   32.102     17.250  156.168   1.861  0.06464 .  
## PopulationKA   34.147     16.224  158.411   2.105  0.03690 *  
## PopulationMA   65.104     15.816  154.657   4.116 6.24e-05 ***
## PopulationMU   35.036     15.873  154.491   2.207  0.02877 *  
## PopulationRE  101.188     16.978  157.147   5.960 1.60e-08 ***
## PopulationUM   69.106     16.725  157.292   4.132 5.83e-05 ***
## PopulationVA   44.498     15.923  155.777   2.795  0.00585 ** 
## PopulationYE   -3.993     16.090  155.387  -0.248  0.80432    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.546                                                 
## PopulatinKA -0.599  0.468                                          
## PopulatinMA -0.581  0.458  0.486                                   
## PopulatinMU -0.600  0.460  0.503  0.497                            
## PopulatinRE -0.560  0.440  0.482  0.463  0.475                     
## PopulatinUM -0.577  0.448  0.495  0.469  0.483  0.463              
## PopulatinVA -0.593  0.465  0.510  0.497  0.505  0.476  0.485       
## PopulatinYE -0.588  0.459  0.500  0.490  0.498  0.470  0.480  0.499

Posnien Lab

anova(WA_F_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population  96035   12004     8    18  1.4376 0.2476
summary(WA_F_L_Posnien_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_F, Supervisor.PI == "Posnien"))
## 
## REML criterion at convergence: 3169.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3762 -0.5835 -0.0034  0.6637  2.5365 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  Line:Population               (Intercept) 7243.4   85.11   
##  ReplicateVial:Line:Population (Intercept)  432.2   20.79   
##  Residual                                  8350.2   91.38   
## Number of obs: 270, groups:  
## Line:Population, 27; ReplicateVial:Line:Population, 27
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2589.01      53.26   18.00  48.609   <2e-16 ***
## PopulationGI   139.19      75.32   18.00   1.848   0.0811 .  
## PopulationKA    50.37      75.32   18.00   0.669   0.5122    
## PopulationMA    69.49      75.32   18.00   0.923   0.3684    
## PopulationMU   -25.56      75.32   18.00  -0.339   0.7383    
## PopulationRE   134.92      75.32   18.00   1.791   0.0901 .  
## PopulationUM    74.02      75.32   18.00   0.983   0.3388    
## PopulationVA   -39.43      75.32   18.00  -0.523   0.6070    
## PopulationYE    68.71      75.32   18.00   0.912   0.3737    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707                                                 
## PopulatinKA -0.707  0.500                                          
## PopulatinMA -0.707  0.500  0.500                                   
## PopulatinMU -0.707  0.500  0.500  0.500                            
## PopulatinRE -0.707  0.500  0.500  0.500  0.500                     
## PopulatinUM -0.707  0.500  0.500  0.500  0.500  0.500              
## PopulatinVA -0.707  0.500  0.500  0.500  0.500  0.500  0.500       
## PopulatinYE -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500
anova(WA_M_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population  36700  4587.5     8    18  0.7652  0.637
summary(WA_M_L_Posnien_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_M, Supervisor.PI == "Posnien"))
## 
## REML criterion at convergence: 3086.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9072 -0.4977  0.0192  0.6488  3.4055 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  Line:Population               (Intercept) 6707.76  81.90   
##  ReplicateVial:Line:Population (Intercept)   72.92   8.54   
##  Residual                                  5995.06  77.43   
## Number of obs: 270, groups:  
## Line:Population, 27; ReplicateVial:Line:Population, 27
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2300.02      49.60   18.00  46.372   <2e-16 ***
## PopulationGI   111.76      70.14   18.00   1.593    0.128    
## PopulationKA    32.56      70.14   18.00   0.464    0.648    
## PopulationMA    81.32      70.14   18.00   1.159    0.261    
## PopulationMU   -33.44      70.14   18.00  -0.477    0.639    
## PopulationRE    74.92      70.14   18.00   1.068    0.300    
## PopulationUM    48.33      70.14   18.00   0.689    0.500    
## PopulationVA    39.70      70.14   18.00   0.566    0.578    
## PopulationYE    49.17      70.14   18.00   0.701    0.492    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707                                                 
## PopulatinKA -0.707  0.500                                          
## PopulatinMA -0.707  0.500  0.500                                   
## PopulatinMU -0.707  0.500  0.500  0.500                            
## PopulatinRE -0.707  0.500  0.500  0.500  0.500                     
## PopulatinUM -0.707  0.500  0.500  0.500  0.500  0.500              
## PopulatinVA -0.707  0.500  0.500  0.500  0.500  0.500  0.500       
## PopulatinYE -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500

Ritchie Lab

anova(WA_F_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 161188   20148     8 27.027  2.2953 0.05084 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_L_Ritchie_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_F, Supervisor.PI == "Ritchie"))
## 
## REML criterion at convergence: 16563.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3958 -0.5231  0.0337  0.6233  3.1535 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  6184     78.64  
##  Line:Population               (Intercept) 17974    134.07  
##  Batch                         (Intercept)  1663     40.79  
##  Residual                                   8778     93.69  
## Number of obs: 1367, groups:  
## ReplicateVial:Line:Population, 132; Line:Population, 47; Batch, 15
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2759.24      74.08   27.97  37.245   <2e-16 ***
## PopulationGI  -172.06      90.56   29.41  -1.900   0.0673 .  
## PopulationKA   -45.09     104.69   27.85  -0.431   0.6700    
## PopulationMA    87.43      99.36   27.78   0.880   0.3864    
## PopulationMU    23.66      99.29   27.88   0.238   0.8134    
## PopulationRE   119.37      99.23   27.93   1.203   0.2391    
## PopulationUM   129.78      99.22   27.99   1.308   0.2015    
## PopulationVA    15.01      98.49   28.09   0.152   0.8799    
## PopulationYE    23.58      97.55   27.05   0.242   0.8108    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.783                                                 
## PopulatinKA -0.708  0.554                                          
## PopulatinMA -0.746  0.584  0.558                                   
## PopulatinMU -0.746  0.584  0.551  0.583                            
## PopulatinRE -0.746  0.584  0.544  0.570  0.564                     
## PopulatinUM -0.745  0.585  0.528  0.557  0.563  0.576              
## PopulatinVA -0.741  0.585  0.525  0.553  0.553  0.573  0.578       
## PopulatinYE -0.735  0.602  0.521  0.549  0.555  0.550  0.561  0.552
anova(WA_M_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population  53563  6695.4     8 27.745  0.9646 0.4828
summary(WA_M_L_Ritchie_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_M, Supervisor.PI == "Ritchie"))
## 
## REML criterion at convergence: 16229.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.9391 -0.4829  0.0696  0.5871  4.6442 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept) 4539.8   67.38   
##  Line:Population               (Intercept) 6326.1   79.54   
##  Batch                         (Intercept)  740.3   27.21   
##  Residual                                  6941.1   83.31   
## Number of obs: 1368, groups:  
## ReplicateVial:Line:Population, 132; Line:Population, 44; Batch, 15
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2448.31      46.86   35.17  52.245   <2e-16 ***
## PopulationGI   -65.05      60.56   24.42  -1.074    0.293    
## PopulationKA   -22.46      62.88   35.11  -0.357    0.723    
## PopulationMA    29.70      62.87   35.07   0.472    0.640    
## PopulationMU   -11.90      62.81   35.11  -0.189    0.851    
## PopulationRE    47.67      62.73   35.02   0.760    0.452    
## PopulationUM    63.32      62.76   35.13   1.009    0.320    
## PopulationVA   -16.05      62.25   34.25  -0.258    0.798    
## PopulationYE   -35.16      61.62   30.97  -0.571    0.572    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.723                                                 
## PopulatinKA -0.745  0.539                                          
## PopulatinMA -0.745  0.539  0.592                                   
## PopulatinMU -0.746  0.540  0.585  0.585                            
## PopulatinRE -0.746  0.540  0.571  0.571  0.565                     
## PopulatinUM -0.745  0.540  0.556  0.557  0.563  0.578              
## PopulatinVA -0.740  0.543  0.552  0.552  0.553  0.575  0.581       
## PopulatinYE -0.734  0.554  0.548  0.548  0.555  0.549  0.562  0.552

Stamenkovic-Radak Lab

## boundary (singular) fit: see ?isSingular
## Warning: Model failed to converge with 1 negative eigenvalue: -7.1e-03
anova(WA_F_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## Population 624737   78092     8 114.6  10.088 1.429e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_L_StamenkovicRadak_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_F, Supervisor.PI == "StamenkovicRadak"))
## 
## REML criterion at convergence: 44876.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.4785 -0.5666  0.0599  0.6399  3.6361 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept) 2434.129 49.34   
##  Line:Population               (Intercept)    6.451  2.54   
##  Batch                         (Intercept)    0.000  0.00   
##  Residual                                  7741.151 87.98   
## Number of obs: 3787, groups:  
## ReplicateVial:Line:Population, 124; Line:Population, 124; Batch, 5
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2780.82      12.95  113.51 214.712  < 2e-16 ***
## PopulationGI    55.22      20.92  114.35   2.639 0.009472 ** 
## PopulationKA    29.48      18.36  114.56   1.606 0.111023    
## PopulationMA    81.80      20.33  114.43   4.023 0.000103 ***
## PopulationMU    66.96      18.60  113.14   3.599 0.000475 ***
## PopulationRE   151.99      19.37  114.23   7.845 2.53e-12 ***
## PopulationUM   102.14      19.01  114.75   5.373 4.12e-07 ***
## PopulationVA    57.63      18.64  114.01   3.092 0.002503 ** 
## PopulationYE    41.29      18.98  113.99   2.176 0.031645 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.619                                                 
## PopulatinKA -0.705  0.437                                          
## PopulatinMA -0.637  0.394  0.449                                   
## PopulatinMU -0.696  0.431  0.491  0.443                            
## PopulatinRE -0.668  0.414  0.472  0.426  0.465                     
## PopulatinUM -0.681  0.422  0.481  0.434  0.474  0.455              
## PopulatinVA -0.695  0.430  0.490  0.443  0.484  0.464  0.473       
## PopulatinYE -0.682  0.422  0.481  0.435  0.475  0.456  0.465  0.474
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
#could drop batch, as it explains nothing
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
## Warning: Model failed to converge with 1 negative eigenvalue: -4.0e-03
anova(WA_M_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 457239   57155     8 112.21  9.5541 5.168e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_M_L_StamenkovicRadak_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_M, Supervisor.PI == "StamenkovicRadak"))
## 
## REML criterion at convergence: 42455.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.2393 -0.5570  0.0480  0.6236  6.0054 
## 
## Random effects:
##  Groups                        Name        Variance  Std.Dev.
##  ReplicateVial:Line:Population (Intercept)    0.3221  0.5676 
##  Line:Population               (Intercept) 1857.6981 43.1010 
##  Batch                         (Intercept)    6.6839  2.5853 
##  Residual                                  5982.2379 77.3449 
## Number of obs: 3662, groups:  
## ReplicateVial:Line:Population, 124; Line:Population, 124; Batch, 5
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2415.63      11.41   58.45 211.661  < 2e-16 ***
## PopulationGI    56.51      18.30  112.86   3.088  0.00253 ** 
## PopulationKA    19.10      16.06  112.79   1.189  0.23692    
## PopulationMA    74.48      17.80  113.38   4.185 5.66e-05 ***
## PopulationMU    52.79      16.28  110.79   3.243  0.00156 ** 
## PopulationRE   121.95      16.98  113.27   7.184 7.65e-11 ***
## PopulationUM    91.59      16.63  113.64   5.506 2.31e-07 ***
## PopulationVA    45.84      16.30  109.93   2.812  0.00584 ** 
## PopulationYE    31.86      16.62  112.43   1.917  0.05774 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.615                                                 
## PopulatinKA -0.702  0.437                                          
## PopulatinMA -0.635  0.396  0.450                                   
## PopulatinMU -0.692  0.431  0.491  0.444                            
## PopulatinRE -0.664  0.413  0.472  0.426  0.465                     
## PopulatinUM -0.678  0.422  0.482  0.435  0.475  0.456              
## PopulatinVA -0.690  0.430  0.490  0.442  0.483  0.464  0.473       
## PopulatinYE -0.678  0.422  0.482  0.435  0.475  0.456  0.465  0.474
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_WAR <- min(d_WA$CentroidSizeRight_micrometers)
max_WAR <- max(d_WA$CentroidSizeRight_micrometers)
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

Onder Lab

anova(WA_F_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 421367   52671     8 151.53  9.0579 3.977e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_R_Onder_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_F, Supervisor.PI == "Onder"))
## 
## REML criterion at convergence: 61106
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.6211 -0.5609  0.0470  0.6255  3.7505 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  806.6   28.40   
##  Line:Population               (Intercept) 3014.8   54.91   
##  Batch                         (Intercept) 1097.0   33.12   
##  Residual                                  5814.9   76.26   
## Number of obs: 5247, groups:  
## ReplicateVial:Line:Population, 518; Line:Population, 167; Batch, 7
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  2815.3200    18.5805   15.4593 151.520  < 2e-16 ***
## PopulationGI   39.4891    20.3804  152.0176   1.938   0.0545 .  
## PopulationKA   44.7269    19.1888  154.5953   2.331   0.0211 *  
## PopulationMA   84.3472    18.6644  150.3413   4.519 1.25e-05 ***
## PopulationMU   45.0927    18.7527  150.6648   2.405   0.0174 *  
## PopulationRE  129.3083    20.0313  152.1392   6.455 1.37e-09 ***
## PopulationUM   86.5324    19.7651  153.1872   4.378 2.21e-05 ***
## PopulationVA   29.8798    18.8068  151.5711   1.589   0.1142    
## PopulationYE    0.3856    18.9925  150.9798   0.020   0.9838    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.468                                                 
## PopulatinKA -0.514  0.469                                          
## PopulatinMA -0.497  0.457  0.485                                   
## PopulatinMU -0.515  0.460  0.503  0.497                            
## PopulatinRE -0.481  0.441  0.484  0.463  0.476                     
## PopulatinUM -0.496  0.449  0.497  0.468  0.483  0.464              
## PopulatinVA -0.508  0.465  0.511  0.497  0.505  0.477  0.486       
## PopulatinYE -0.504  0.459  0.501  0.490  0.498  0.472  0.480  0.499
anova(WA_M_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 288086   36011     8 156.05  7.6824 1.188e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_M_R_Onder_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_M, Supervisor.PI == "Onder"))
## 
## REML criterion at convergence: 59827.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1368 -0.5848  0.0327  0.6114  6.0665 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  874.6   29.57   
##  Line:Population               (Intercept) 2102.4   45.85   
##  Batch                         (Intercept)  282.9   16.82   
##  Residual                                  4687.4   68.46   
## Number of obs: 5230, groups:  
## ReplicateVial:Line:Population, 518; Line:Population, 167; Batch, 7
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  2444.990     13.290   35.827 183.969  < 2e-16 ***
## PopulationGI   32.847     17.427  156.488   1.885  0.06131 .  
## PopulationKA   36.345     16.381  158.647   2.219  0.02793 *  
## PopulationMA   64.809     15.983  154.890   4.055 7.92e-05 ***
## PopulationMU   34.311     16.034  154.618   2.140  0.03393 *  
## PopulationRE  102.099     17.153  157.513   5.952 1.66e-08 ***
## PopulationUM   68.797     16.894  157.624   4.072 7.35e-05 ***
## PopulationVA   44.416     16.087  156.079   2.761  0.00645 ** 
## PopulationYE   -3.748     16.257  155.670  -0.231  0.81799    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.559                                                 
## PopulatinKA -0.614  0.468                                          
## PopulatinMA -0.596  0.458  0.487                                   
## PopulatinMU -0.615  0.461  0.503  0.497                            
## PopulatinRE -0.574  0.440  0.482  0.463  0.475                     
## PopulatinUM -0.591  0.448  0.495  0.470  0.483  0.462              
## PopulatinVA -0.608  0.465  0.510  0.497  0.505  0.476  0.485       
## PopulatinYE -0.602  0.459  0.500  0.490  0.498  0.470  0.479  0.499

Posnien Lab

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -1.1e-04
anova(WA_F_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population  97450   12181     8    18  1.4012 0.2615
summary(WA_F_R_Posnien_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_F, Supervisor.PI == "Posnien"))
## 
## REML criterion at convergence: 3180
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.88515 -0.60816  0.01116  0.64507  2.45427 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  Line:Population               (Intercept) 7037.2   83.89   
##  ReplicateVial:Line:Population (Intercept)  718.1   26.80   
##  Residual                                  8693.5   93.24   
## Number of obs: 270, groups:  
## Line:Population, 27; ReplicateVial:Line:Population, 27
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2584.98      53.62   18.00  48.211   <2e-16 ***
## PopulationGI   140.26      75.83   18.00   1.850   0.0808 .  
## PopulationKA    52.35      75.83   18.00   0.690   0.4988    
## PopulationMA    72.29      75.83   18.00   0.953   0.3530    
## PopulationMU   -22.66      75.83   18.00  -0.299   0.7685    
## PopulationRE   135.06      75.83   18.00   1.781   0.0918 .  
## PopulationUM    75.52      75.83   18.00   0.996   0.3325    
## PopulationVA   -37.05      75.83   18.00  -0.489   0.6310    
## PopulationYE    72.22      75.83   18.00   0.952   0.3535    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707                                                 
## PopulatinKA -0.707  0.500                                          
## PopulatinMA -0.707  0.500  0.500                                   
## PopulatinMU -0.707  0.500  0.500  0.500                            
## PopulatinRE -0.707  0.500  0.500  0.500  0.500                     
## PopulatinUM -0.707  0.500  0.500  0.500  0.500  0.500              
## PopulatinVA -0.707  0.500  0.500  0.500  0.500  0.500  0.500       
## PopulatinYE -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate  Hessian with 1 negative eigenvalues
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
anova(WA_M_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population  32589  4073.6     8    18  0.6753 0.7071
summary(WA_M_R_Posnien_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_M, Supervisor.PI == "Posnien"))
## 
## REML criterion at convergence: 3088.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0851 -0.5566 -0.0153  0.6569  3.5274 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  Line:Population               (Intercept)   38.41   6.198  
##  ReplicateVial:Line:Population (Intercept) 6927.30  83.230  
##  Residual                                  6032.05  77.666  
## Number of obs: 270, groups:  
## Line:Population, 27; ReplicateVial:Line:Population, 27
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2296.73      50.23   18.00  45.725   <2e-16 ***
## PopulationGI   108.33      71.03   18.00   1.525    0.145    
## PopulationKA    34.91      71.03   18.00   0.492    0.629    
## PopulationMA    86.17      71.03   18.00   1.213    0.241    
## PopulationMU   -22.87      71.03   18.00  -0.322    0.751    
## PopulationRE    78.26      71.03   18.00   1.102    0.285    
## PopulationUM    52.40      71.03   18.00   0.738    0.470    
## PopulationVA    37.49      71.03   18.00   0.528    0.604    
## PopulationYE    48.05      71.03   18.00   0.676    0.507    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707                                                 
## PopulatinKA -0.707  0.500                                          
## PopulatinMA -0.707  0.500  0.500                                   
## PopulatinMU -0.707  0.500  0.500  0.500                            
## PopulatinRE -0.707  0.500  0.500  0.500  0.500                     
## PopulatinUM -0.707  0.500  0.500  0.500  0.500  0.500              
## PopulatinVA -0.707  0.500  0.500  0.500  0.500  0.500  0.500       
## PopulatinYE -0.707  0.500  0.500  0.500  0.500  0.500  0.500  0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate  Hessian with 1 negative eigenvalues

Ritchie Lab

anova(WA_F_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 163375   20422     8 26.835  2.3305 0.04807 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_R_Ritchie_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_F, Supervisor.PI == "Ritchie"))
## 
## REML criterion at convergence: 16564.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.4488 -0.5249  0.0615  0.6440  3.1927 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  6492     80.58  
##  Line:Population               (Intercept) 17468    132.17  
##  Batch                         (Intercept)  1632     40.39  
##  Residual                                   8763     93.61  
## Number of obs: 1367, groups:  
## ReplicateVial:Line:Population, 132; Line:Population, 47; Batch, 15
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2777.36      73.35   27.73  37.864   <2e-16 ***
## PopulationGI  -165.69      89.72   29.22  -1.847   0.0749 .  
## PopulationKA   -45.51     103.65   27.61  -0.439   0.6640    
## PopulationMA    98.04      98.38   27.54   0.997   0.3277    
## PopulationMU    28.33      98.31   27.64   0.288   0.7753    
## PopulationRE   118.86      98.25   27.69   1.210   0.2366    
## PopulationUM   136.27      98.24   27.75   1.387   0.1764    
## PopulationVA    16.64      97.51   27.86   0.171   0.8658    
## PopulationYE    20.02      96.58   26.86   0.207   0.8374    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.782                                                 
## PopulatinKA -0.708  0.554                                          
## PopulatinMA -0.746  0.583  0.559                                   
## PopulatinMU -0.746  0.584  0.551  0.583                            
## PopulatinRE -0.746  0.584  0.544  0.570  0.564                     
## PopulatinUM -0.745  0.584  0.528  0.557  0.563  0.576              
## PopulatinVA -0.741  0.585  0.525  0.553  0.553  0.573  0.579       
## PopulatinYE -0.735  0.602  0.521  0.549  0.555  0.550  0.561  0.552
anova(WA_M_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population  57030  7128.7     8 27.792  1.0373 0.4328
summary(WA_M_R_Ritchie_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_M, Supervisor.PI == "Ritchie"))
## 
## REML criterion at convergence: 16215.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.9851 -0.5112  0.0484  0.5839  4.7200 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept) 4472.4   66.88   
##  Line:Population               (Intercept) 6265.6   79.16   
##  Batch                         (Intercept)  642.8   25.35   
##  Residual                                  6872.5   82.90   
## Number of obs: 1368, groups:  
## ReplicateVial:Line:Population, 132; Line:Population, 44; Batch, 15
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  2462.726     46.397   35.179  53.080   <2e-16 ***
## PopulationGI  -61.574     60.208   24.479  -1.023    0.316    
## PopulationKA  -17.058     62.255   35.130  -0.274    0.786    
## PopulationMA   36.423     62.238   35.091   0.585    0.562    
## PopulationMU   -5.495     62.192   35.119  -0.088    0.930    
## PopulationRE   53.814     62.121   35.030   0.866    0.392    
## PopulationUM   70.115     62.159   35.138   1.128    0.267    
## PopulationVA  -13.902     61.707   34.271  -0.225    0.823    
## PopulationYE  -31.154     61.155   31.080  -0.509    0.614    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.726                                                 
## PopulatinKA -0.745  0.541                                          
## PopulatinMA -0.745  0.541  0.588                                   
## PopulatinMU -0.746  0.542  0.582  0.582                            
## PopulatinRE -0.746  0.542  0.569  0.570  0.564                     
## PopulatinUM -0.745  0.542  0.556  0.556  0.562  0.576              
## PopulatinVA -0.741  0.544  0.553  0.553  0.553  0.573  0.578       
## PopulatinYE -0.736  0.554  0.549  0.549  0.555  0.550  0.561  0.552

Stamenkovic-Radak Lab

## boundary (singular) fit: see ?isSingular
anova(WA_F_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 652123   81515     8 114.54  10.577 4.982e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_R_StamenkovicRadak_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_F, Supervisor.PI == "StamenkovicRadak"))
## 
## REML criterion at convergence: 44862
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7152 -0.5616  0.0783  0.6287  3.6999 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept) 2473.834 49.738  
##  Line:Population               (Intercept)    8.832  2.972  
##  Batch                         (Intercept)    0.000  0.000  
##  Residual                                  7707.008 87.790  
## Number of obs: 3787, groups:  
## ReplicateVial:Line:Population, 124; Line:Population, 124; Batch, 5
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2775.53      13.05  113.47 212.685  < 2e-16 ***
## PopulationGI    58.19      21.08  114.29   2.761 0.006725 ** 
## PopulationKA    37.52      18.50  114.50   2.029 0.044821 *  
## PopulationMA    86.50      20.49  114.37   4.223 4.87e-05 ***
## PopulationMU    74.74      18.75  113.11   3.987 0.000119 ***
## PopulationRE   157.73      19.52  114.18   8.080 7.48e-13 ***
## PopulationUM   111.31      19.16  114.69   5.811 5.67e-08 ***
## PopulationVA    60.93      18.78  113.96   3.244 0.001546 ** 
## PopulationYE    47.88      19.12  113.95   2.504 0.013710 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.619                                                 
## PopulatinKA -0.705  0.437                                          
## PopulatinMA -0.637  0.394  0.449                                   
## PopulatinMU -0.696  0.431  0.491  0.443                            
## PopulatinRE -0.668  0.414  0.472  0.426  0.465                     
## PopulatinUM -0.681  0.422  0.481  0.434  0.474  0.455              
## PopulatinVA -0.695  0.430  0.490  0.443  0.484  0.465  0.473       
## PopulatinYE -0.682  0.422  0.481  0.435  0.475  0.456  0.465  0.474
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
# could drop batch, as it explains nothing 
## boundary (singular) fit: see ?isSingular
## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+02
anova(WA_M_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)    
## Population 447007   55876     8 114.12   9.518 5.12e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_M_R_StamenkovicRadak_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +  
##     (1 | Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_WA_M, Supervisor.PI == "StamenkovicRadak"))
## 
## REML criterion at convergence: 42394.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.2672 -0.5646  0.0593  0.6360  5.9328 
## 
## Random effects:
##  Groups                        Name        Variance  Std.Dev. 
##  ReplicateVial:Line:Population (Intercept) 1.971e+03 44.393013
##  Line:Population               (Intercept) 5.134e+00  2.265812
##  Batch                         (Intercept) 2.508e-06  0.001584
##  Residual                                  5.871e+03 76.619391
## Number of obs: 3662, groups:  
## ReplicateVial:Line:Population, 124; Line:Population, 124; Batch, 5
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2414.97      11.64  113.02 207.545  < 2e-16 ***
## PopulationGI    54.10      18.78  113.55   2.880  0.00476 ** 
## PopulationKA    19.86      16.49  113.90   1.205  0.23084    
## PopulationMA    73.65      18.26  113.66   4.034 9.97e-05 ***
## PopulationMU    54.91      16.72  112.80   3.284  0.00136 ** 
## PopulationRE   125.69      17.43  114.26   7.213 6.42e-11 ***
## PopulationUM    94.90      17.07  114.05   5.559 1.81e-07 ***
## PopulationVA    47.87      16.75  113.55   2.858  0.00507 ** 
## PopulationYE    33.86      17.06  113.79   1.985  0.04960 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.619                                                 
## PopulatinKA -0.706  0.437                                          
## PopulatinMA -0.637  0.395  0.450                                   
## PopulatinMU -0.696  0.431  0.491  0.444                            
## PopulatinRE -0.668  0.414  0.471  0.426  0.465                     
## PopulatinUM -0.682  0.422  0.481  0.434  0.474  0.455              
## PopulatinVA -0.695  0.430  0.490  0.443  0.484  0.464  0.474       
## PopulatinYE -0.682  0.422  0.481  0.435  0.475  0.455  0.465  0.474
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

Lab correlations

There are 9 populations and 5 isofemale lines for females and 9 populations and 5 isofemale lines for males have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Left

Right

Fecundity

For a detailed description of tables, plots, linear models and outputs, please refer here 

#Note that the trait has been phenotyped only in females.

Contributors

Billeter Lab : Xiaocui Wang, Tiphaine Bailly, Mario Mira, Jean-Christophe Billeter

Fricke Lab : Claudia Fricke

Data Reformatting

Reading data in R

d_Fec <- read.csv("MasterSheets_Oct21_git/FEC_MasterSheet_Oct21.csv")
str(d_Fec)
## 'data.frame':    1725 obs. of  13 variables:
##  $ Supervisor.PI        : chr  "Billeter" "Billeter" "Billeter" "Billeter" ...
##  $ Diet                 : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch                : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population           : chr  "AK" "AK" "AK" "AK" ...
##  $ Line                 : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Individual           : int  1 2 3 4 5 1 2 3 4 5 ...
##  $ NumberOfAdultsEclosed: int  206 75 54 58 278 162 0 101 145 188 ...
##  $ Notes                : chr  NA NA NA NA ...
##  $ Censor               : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Country              : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude             : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude            : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude             : int  88 88 88 88 88 88 88 88 88 88 ...

Factors need reformatting (i.e. Supervisor.PI should be coded as a factor, not character).

str(d_Fec)
## 'data.frame':    1725 obs. of  13 variables:
##  $ Supervisor.PI        : chr  "Billeter" "Billeter" "Billeter" "Billeter" ...
##  $ Diet                 : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch                : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population           : chr  "AK" "AK" "AK" "AK" ...
##  $ Line                 : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Individual           : int  1 2 3 4 5 1 2 3 4 5 ...
##  $ NumberOfAdultsEclosed: int  206 75 54 58 278 162 0 101 145 188 ...
##  $ Notes                : chr  NA NA NA NA ...
##  $ Censor               : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Country              : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude             : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude            : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude             : int  88 88 88 88 88 88 88 88 88 88 ...
d_Fec$Supervisor.PI <- as.factor(d_Fec$Supervisor.PI)
d_Fec$Diet <- as.factor(d_Fec$Diet)
d_Fec$Batch <- as.factor(d_Fec$Batch)
d_Fec$Population <- as.factor(d_Fec$Population)
d_Fec$Population_Lat <- factor(d_Fec$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Fec$Population_Lon <- factor(d_Fec$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Fec$Population_Alt <- factor(d_Fec$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Fec$Line <- as.factor(d_Fec$Line)
d_Fec$NumberOfAdultsEclosed <- as.numeric(d_Fec$NumberOfAdultsEclosed)
d_Fec$Censor <- as.factor(d_Fec$Censor)
str(d_Fec)
## 'data.frame':    1725 obs. of  16 variables:
##  $ Supervisor.PI        : Factor w/ 2 levels "Billeter","Fricke": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet                 : Factor w/ 2 levels "NS","S": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch                : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population           : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Line                 : Factor w/ 169 levels "AK1","AK10","AK11",..: 1 1 1 1 1 11 11 11 11 11 ...
##  $ Individual           : int  1 2 3 4 5 1 2 3 4 5 ...
##  $ NumberOfAdultsEclosed: num  206 75 54 58 278 162 0 101 145 188 ...
##  $ Notes                : chr  NA NA NA NA ...
##  $ Censor               : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Country              : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude             : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude            : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude             : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat       : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon       : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt       : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_Fec <- subset(d_Fec, Censor == "0")

Now they should be in the correct format, see below.

str(d_Fec)
## 'data.frame':    1721 obs. of  16 variables:
##  $ Supervisor.PI        : Factor w/ 2 levels "Billeter","Fricke": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet                 : Factor w/ 2 levels "NS","S": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch                : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population           : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Line                 : Factor w/ 169 levels "AK1","AK10","AK11",..: 1 1 1 1 1 11 11 11 11 11 ...
##  $ Individual           : int  1 2 3 4 5 1 2 3 4 5 ...
##  $ NumberOfAdultsEclosed: num  206 75 54 58 278 162 0 101 145 188 ...
##  $ Notes                : chr  NA NA NA NA ...
##  $ Censor               : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Country              : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude             : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude            : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude             : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat       : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon       : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt       : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics for fecundity at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_Fec <- min(d_Fec$NumberOfAdultsEclosed)
max_Fec <- max(d_Fec$NumberOfAdultsEclosed)

y-axis is scaled by the minimum (0) and maximum (306) values in the full data set.

Billeter Lab

## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(Fec_lmer_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population  61874  7734.2     8 148.39  2.7992 0.006461 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Fec_lmer_Billeter)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NumberOfAdultsEclosed ~ Population + (1 | Population:Line)
##    Data: d_Fec[d_Fec$Supervisor.PI == "Billeter", ]
## 
## REML criterion at convergence: 8773.2
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.02323 -0.55643 -0.03579  0.53645  2.97008 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Population:Line (Intercept) 1113     33.36   
##  Residual                    2763     52.56   
## Number of obs: 805, groups:  Population:Line, 160
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)    97.067      9.154 149.480  10.603   <2e-16 ***
## PopulationGI  -22.874     13.904 146.434  -1.645   0.1021    
## PopulationKA   12.903     12.928 148.886   0.998   0.3198    
## PopulationMA    2.053     13.460 147.925   0.152   0.8790    
## PopulationMU   -3.225     13.226 152.369  -0.244   0.8077    
## PopulationRE  -29.213     14.231 148.404  -2.053   0.0419 *  
## PopulationUM    5.760     13.959 148.978   0.413   0.6805    
## PopulationVA  -10.734     12.894 147.238  -0.832   0.4065    
## PopulationYE  -33.130     12.963 150.368  -2.556   0.0116 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.658                                                 
## PopulatinKA -0.708  0.466                                          
## PopulatinMA -0.680  0.448  0.482                                   
## PopulatinMU -0.692  0.456  0.490  0.471                            
## PopulatinRE -0.643  0.424  0.455  0.437  0.445                     
## PopulatinUM -0.656  0.432  0.464  0.446  0.454  0.422              
## PopulatinVA -0.710  0.467  0.503  0.483  0.491  0.457  0.466       
## PopulatinYE -0.706  0.465  0.500  0.480  0.489  0.454  0.463  0.501

Fricke Lab

## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(Fec_lmer_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population 4506.3  563.29     8 146.12  0.4525 0.8873
summary(Fec_lmer_Fricke)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NumberOfAdultsEclosed ~ Population + (1 | Population:Line) +  
##     (1 | Batch)
##    Data: d_Fec[d_Fec$Supervisor.PI == "Fricke", ]
## 
## REML criterion at convergence: 9249.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8102 -0.6481 -0.0705  0.5894  3.4523 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Population:Line (Intercept)  455.06  21.332  
##  Batch           (Intercept)   41.26   6.423  
##  Residual                    1244.86  35.283  
## Number of obs: 916, groups:  Population:Line, 161; Batch, 8
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)    61.074      6.779  95.724   9.009 2.05e-14 ***
## PopulationGI  -10.583      9.374 152.556  -1.129    0.261    
## PopulationKA  -10.298      8.769 143.094  -1.174    0.242    
## PopulationMA   -5.089      8.673 146.815  -0.587    0.558    
## PopulationMU   -4.207      8.875 147.002  -0.474    0.636    
## PopulationRE   -3.612      9.302 151.133  -0.388    0.698    
## PopulationUM  -14.075      8.963 148.011  -1.570    0.118    
## PopulationVA   -7.192      8.825 149.162  -0.815    0.416    
## PopulationYE   -8.232      8.793 151.818  -0.936    0.351    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.623                                                 
## PopulatinKA -0.665  0.483                                          
## PopulatinMA -0.671  0.486  0.522                                   
## PopulatinMU -0.667  0.475  0.511  0.513                            
## PopulatinRE -0.628  0.455  0.487  0.495  0.478                     
## PopulatinUM -0.653  0.472  0.505  0.514  0.495  0.479              
## PopulatinVA -0.668  0.477  0.513  0.521  0.510  0.484  0.503       
## PopulatinYE -0.668  0.480  0.514  0.522  0.507  0.486  0.506  0.511

Lab correlations

There are 152 populations and 152 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Lifespan

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Flatt Lab : Esra Durmaz, Envel Kerdaffrec, Thibault Schowing, Virginie Thieu, Marisa Rodrigues, Thomas Flatt

Parsch Lab : Amanda Glaser-Schmitt, Eliza Argyridou, John Parsch

Pasyukova Lab : Natalia Roshina, Alexander Symonenko, Mikhail Trostnikov, Evgenia Tsybul’ko, Ekaterina Veselkina, Olga Rybina, Elena Pasyukova

Data Reformatting

d_LS_L <- read.csv("MasterSheets_Oct21_git/LSL_MasterSheet_Oct21.csv")
str(d_LS_L)
## 'data.frame':    39844 obs. of  15 variables:
##  $ Supervisor.PI      : chr  "Pasyukova" "Pasyukova" "Pasyukova" "Pasyukova" ...
##  $ Diet               : chr  "S" "S" "S" "S" ...
##  $ Batch              : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population         : chr  "AK" "AK" "AK" "AK" ...
##  $ Line               : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Sex                : chr  "F" "F" "F" "F" ...
##  $ ReplicateVialOld   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial      : chr  "Pasyukova_1_AK1_1" "Pasyukova_1_AK1_1" "Pasyukova_1_AK1_1" "Pasyukova_1_AK1_1" ...
##  $ Individual         : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Censor             : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ LSL_AgeAtDeath_days: int  10 37 41 42 42 45 45 45 45 46 ...
##  $ Country            : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude           : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude          : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude           : int  88 88 88 88 88 88 88 88 88 88 ...
d_LS_L$Supervisor.PI <- as.factor(d_LS_L$Supervisor.PI)
d_LS_L$Diet <- as.factor(d_LS_L$Diet)
d_LS_L$Batch <- as.factor(d_LS_L$Batch)
d_LS_L$Population <- as.factor(d_LS_L$Population)
d_LS_L$Population_Lat <- factor(d_LS_L$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_LS_L$Population_Lon <- factor(d_LS_L$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_LS_L$Population_Alt <- factor(d_LS_L$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_LS_L$Line <- as.factor(d_LS_L$Line)
d_LS_L$ReplicateVial <- as.factor(d_LS_L$ReplicateVial)
d_LS_L$LSL_AgeAtDeath_days <- as.numeric(d_LS_L$LSL_AgeAtDeath_days)
d_LS_L$Censor <- as.factor(d_LS_L$Censor)
str(d_LS_L)
## 'data.frame':    39844 obs. of  18 variables:
##  $ Supervisor.PI      : Factor w/ 2 levels "Parsch","Pasyukova": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Diet               : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Batch              : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population         : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Line               : Factor w/ 171 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex                : chr  "F" "F" "F" "F" ...
##  $ ReplicateVialOld   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial      : Factor w/ 2032 levels "Parsch_1_AK1_1",..: 681 681 681 681 681 681 681 681 681 681 ...
##  $ Individual         : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Censor             : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ LSL_AgeAtDeath_days: num  10 37 41 42 42 45 45 45 45 46 ...
##  $ Country            : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude           : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude          : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude           : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat     : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon     : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt     : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_LS_L_F <-subset(d_LS_L,Sex=='F')
d_LS_L_M <-subset(d_LS_L,Sex=='M')
d_LS_P <- read.csv("MasterSheets_Oct21_git/LSP_MasterSheet_Oct21.csv")
str(d_LS_P)
## 'data.frame':    10612 obs. of  14 variables:
##  $ Supervisor.PI      : chr  "Flatt" "Flatt" "Flatt" "Flatt" ...
##  $ Diet               : chr  "S" "S" "S" "S" ...
##  $ Batch              : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population         : chr  "AK" "AK" "AK" "AK" ...
##  $ Sex                : chr  "F" "F" "F" "F" ...
##  $ ReplicateCageOld   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateCage      : chr  "Flatt_AK_F_1" "Flatt_AK_F_1" "Flatt_AK_F_1" "Flatt_AK_F_1" ...
##  $ Individual         : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Censor             : int  1 1 1 1 0 0 0 1 0 0 ...
##  $ LSP_AgeAtDeath_days: int  11 11 17 23 26 26 33 33 40 43 ...
##  $ Country            : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude           : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude          : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude           : int  88 88 88 88 88 88 88 88 88 88 ...
d_LS_P$Supervisor.PI <- as.factor(d_LS_P$Supervisor.PI)
d_LS_P$Diet <- as.factor(d_LS_P$Diet)
d_LS_P$Batch <- as.factor(d_LS_P$Batch)
d_LS_P$Population <- as.factor(d_LS_P$Population)
d_LS_P$Population_Lat <- factor(d_LS_P$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_LS_P$Population_Lon <- factor(d_LS_P$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_LS_P$Population_Alt <- factor(d_LS_P$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_LS_P$ReplicateCage <- as.factor(d_LS_P$ReplicateCage)
d_LS_P$LSP_AgeAtDeath_days <- as.numeric(d_LS_P$LSP_AgeAtDeath_days)
d_LS_P$Censor <- as.factor(d_LS_P$Censor)
str(d_LS_P)
## 'data.frame':    10612 obs. of  17 variables:
##  $ Supervisor.PI      : Factor w/ 1 level "Flatt": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet               : Factor w/ 1 level "S": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch              : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population         : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex                : chr  "F" "F" "F" "F" ...
##  $ ReplicateCageOld   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateCage      : Factor w/ 180 levels "Flatt_AK_F_1",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Individual         : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Censor             : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ...
##  $ LSP_AgeAtDeath_days: num  11 11 17 23 26 26 33 33 40 43 ...
##  $ Country            : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude           : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude          : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude           : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat     : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon     : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt     : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_LS_P_F <-subset(d_LS_P,Sex=='F')
d_LS_P_M <-subset(d_LS_P,Sex=='M')
d_LS_M <- read.csv("MasterSheets_Oct21_git/LSM_MasterSheet_Oct21.csv")
str(d_LS_M)
## 'data.frame':    50456 obs. of  12 variables:
##  $ Supervisor.PI      : chr  "Pasyukova" "Pasyukova" "Pasyukova" "Pasyukova" ...
##  $ Diet               : chr  "S" "S" "S" "S" ...
##  $ Batch              : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population         : chr  "AK" "AK" "AK" "AK" ...
##  $ Sex                : chr  "F" "F" "F" "F" ...
##  $ Individual         : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Censor             : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ LSM_AgeAtDeath_days: int  10 37 41 42 42 45 45 45 45 46 ...
##  $ Country            : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude           : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude          : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude           : int  88 88 88 88 88 88 88 88 88 88 ...
d_LS_M$Supervisor.PI <- as.factor(d_LS_M$Supervisor.PI)
d_LS_M$Diet <- as.factor(d_LS_M$Diet)
d_LS_M$Batch <- as.factor(d_LS_M$Batch)
d_LS_M$Population <- as.factor(d_LS_M$Population)
d_LS_M$Population_Lat <- factor(d_LS_M$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_LS_M$Population_Lon <- factor(d_LS_M$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_LS_M$Population_Alt <- factor(d_LS_M$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_LS_M$LSM_AgeAtDeath_days <- as.numeric(d_LS_M$LSM_AgeAtDeath_days)
d_LS_M$Censor <- as.factor(d_LS_M$Censor)
str(d_LS_M)
## 'data.frame':    50456 obs. of  15 variables:
##  $ Supervisor.PI      : Factor w/ 3 levels "Flatt","Parsch",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ Diet               : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Batch              : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population         : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex                : chr  "F" "F" "F" "F" ...
##  $ Individual         : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Censor             : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ LSM_AgeAtDeath_days: num  10 37 41 42 42 45 45 45 45 46 ...
##  $ Country            : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude           : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude          : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude           : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat     : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon     : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt     : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_LS_M_F <-subset(d_LS_M,Sex=='F')
d_LS_M_M <-subset(d_LS_M,Sex=='M')

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_LS <- min(d_LS_M$LSM_AgeAtDeath_days)
max_LS <- max(d_LS_M$LSM_AgeAtDeath_days)

y-axis is scaled by the minimum (1) and maximum (112) values in the full data set.

Flatt Lab

anova(LS_P_F_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population  48851  6106.4     8 80.887  38.896 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_P_F_Flatt_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LSP_AgeAtDeath_days ~ Population + (1 | Population:ReplicateCage)
##    Data: filter(d_LS_P_F, Censor == "0" & Supervisor.PI == "Flatt")
## 
## REML criterion at convergence: 36116.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.3943 -0.6327 -0.0087  0.6402  3.5292 
## 
## Random effects:
##  Groups                   Name        Variance Std.Dev.
##  Population:ReplicateCage (Intercept)   5.11    2.261  
##  Residual                             156.99   12.530  
## Number of obs: 4567, groups:  Population:ReplicateCage, 90
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   60.0334     0.9132  83.3744  65.740  < 2e-16 ***
## PopulationGI -17.0858     1.2783  80.0349 -13.366  < 2e-16 ***
## PopulationKA  -4.6088     1.2802  80.5100  -3.600 0.000548 ***
## PopulationMA  -9.5139     1.2873  82.3008  -7.390 1.09e-10 ***
## PopulationMU  -2.6133     1.2894  82.8120  -2.027 0.045911 *  
## PopulationRE -14.4260     1.2849  81.6893 -11.228  < 2e-16 ***
## PopulationUM  -8.3626     1.2968  84.6172  -6.449 6.66e-09 ***
## PopulationVA  -3.7623     1.2899  82.8853  -2.917 0.004548 ** 
## PopulationYE -10.3710     1.2829  81.2162  -8.084 5.05e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.714                                                 
## PopulatinKA -0.713  0.510                                          
## PopulatinMA -0.709  0.507  0.506                                   
## PopulatinMU -0.708  0.506  0.505  0.502                            
## PopulatinRE -0.711  0.508  0.507  0.504  0.503                     
## PopulatinUM -0.704  0.503  0.502  0.500  0.499  0.500              
## PopulatinVA -0.708  0.506  0.505  0.502  0.501  0.503  0.499       
## PopulatinYE -0.712  0.508  0.508  0.505  0.504  0.506  0.501  0.504
anova(LS_P_M_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 7270.2  908.78     8 80.282  5.1988 3.022e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_P_M_Flatt_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LSP_AgeAtDeath_days ~ Population + (1 | Population:ReplicateCage)
##    Data: filter(d_LS_P_M, Censor == "0" & Supervisor.PI == "Flatt")
## 
## REML criterion at convergence: 35650
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9146 -0.6656 -0.0467  0.6755  3.0925 
## 
## Random effects:
##  Groups                   Name        Variance Std.Dev.
##  Population:ReplicateCage (Intercept)   3.827   1.956  
##  Residual                             174.806  13.221  
## Number of obs: 4450, groups:  Population:ReplicateCage, 90
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   51.6194     0.8739 86.0482  59.065  < 2e-16 ***
## PopulationGI  -0.7746     1.2196 81.5439  -0.635  0.52715    
## PopulationKA   4.4450     1.2221 82.2841   3.637  0.00048 ***
## PopulationMA   1.4199     1.2193 81.5433   1.165  0.24759    
## PopulationMU   4.0254     1.2309 84.7065   3.270  0.00155 ** 
## PopulationRE  -1.1984     1.2198 81.6147  -0.982  0.32880    
## PopulationUM   0.7366     1.2216 82.0648   0.603  0.54817    
## PopulationVA   0.4164     1.2399 87.0018   0.336  0.73782    
## PopulationYE   0.7089     1.2213 82.0555   0.580  0.56318    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.717                                                 
## PopulatinKA -0.715  0.512                                          
## PopulatinMA -0.717  0.514  0.513                                   
## PopulatinMU -0.710  0.509  0.508  0.509                            
## PopulatinRE -0.716  0.513  0.512  0.514  0.509                     
## PopulatinUM -0.715  0.513  0.512  0.513  0.508  0.513              
## PopulatinVA -0.705  0.505  0.504  0.505  0.500  0.505  0.504       
## PopulatinYE -0.716  0.513  0.512  0.513  0.508  0.513  0.512  0.504

Parsch Lab

anova(LS_L_F_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population  17537  2192.2     8 126.41   8.199 6.785e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_L_F_Parsch_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## LSL_AgeAtDeath_days ~ Population + (1 | Batch) + (1 | Population:Line) +  
##     (1 | Line:ReplicateVial)
##    Data: filter(d_LS_L_F, Censor == "0" & Supervisor.PI == "Parsch")
## 
## REML criterion at convergence: 42206.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6820 -0.5293  0.1220  0.6479  2.5964 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  Line:ReplicateVial (Intercept)  46.61    6.827  
##  Population:Line    (Intercept)  21.52    4.639  
##  Batch              (Intercept)  12.92    3.594  
##  Residual                       267.37   16.351  
## Number of obs: 4934, groups:  
## Line:ReplicateVial, 679; Population:Line, 135; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)    59.625      3.000   1.861  19.878   0.0035 ** 
## PopulationGI   -4.716      2.260 125.623  -2.087   0.0389 *  
## PopulationKA   -2.759      2.259 125.474  -1.221   0.2243    
## PopulationMA  -12.319      2.269 127.434  -5.430 2.75e-07 ***
## PopulationMU   -5.612      2.259 125.275  -2.484   0.0143 *  
## PopulationRE  -13.187      2.256 124.844  -5.844 4.16e-08 ***
## PopulationUM  -10.542      2.260 125.477  -4.665 7.77e-06 ***
## PopulationVA   -3.205      2.250 123.444  -1.424   0.1568    
## PopulationYE   -5.034      2.264 126.542  -2.223   0.0280 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.374                                                 
## PopulatinKA -0.375  0.496                                          
## PopulatinMA -0.373  0.494  0.497                                   
## PopulatinMU -0.375  0.497  0.498  0.496                            
## PopulatinRE -0.375  0.497  0.499  0.497  0.499                     
## PopulatinUM -0.375  0.496  0.498  0.496  0.498  0.499              
## PopulatinVA -0.376  0.498  0.500  0.498  0.500  0.501  0.500       
## PopulatinYE -0.374  0.495  0.497  0.495  0.497  0.498  0.497  0.499
anova(LS_L_M_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population  16320    2040     8 126.51  5.7368 3.127e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_L_M_Parsch_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## LSL_AgeAtDeath_days ~ Population + (1 | Batch) + (1 | Population:Line) +  
##     (1 | Line:ReplicateVial)
##    Data: filter(d_LS_L_M, Censor == "0" & Supervisor.PI == "Parsch")
## 
## REML criterion at convergence: 42301.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2393 -0.5900  0.0897  0.6900  2.7320 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  Line:ReplicateVial (Intercept)  13.150   3.626  
##  Population:Line    (Intercept)  34.973   5.914  
##  Batch              (Intercept)   2.336   1.528  
##  Residual                       355.597  18.857  
## Number of obs: 4821, groups:  
## Line:ReplicateVial, 679; Population:Line, 135; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   60.4729     2.0895  11.2354  28.942 6.58e-12 ***
## PopulationGI  -9.1042     2.5261 127.0376  -3.604 0.000448 ***
## PopulationKA  -3.6781     2.5280 127.3877  -1.455 0.148142    
## PopulationMA  -9.5146     2.5298 127.6972  -3.761 0.000257 ***
## PopulationMU  -1.8715     2.5213 126.0661  -0.742 0.459297    
## PopulationRE -11.2522     2.5232 126.4047  -4.460 1.79e-05 ***
## PopulationUM  -3.5999     2.5290 127.6173  -1.423 0.157049    
## PopulationVA  -0.7315     2.5259 126.9086  -0.290 0.772607    
## PopulationYE  -8.7973     2.5284 127.4703  -3.479 0.000688 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.605                                                 
## PopulatinKA -0.606  0.500                                          
## PopulatinMA -0.606  0.500  0.501                                   
## PopulatinMU -0.607  0.502  0.502  0.502                            
## PopulatinRE -0.607  0.501  0.502  0.502  0.503                     
## PopulatinUM -0.605  0.500  0.501  0.501  0.502  0.502              
## PopulatinVA -0.606  0.501  0.501  0.501  0.502  0.502  0.501       
## PopulatinYE -0.606  0.500  0.501  0.501  0.502  0.502  0.501  0.501

Pasyukova Lab

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0111326 (tol = 0.002, component 1)
anova(LS_L_F_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 4191.7  523.96     8 160.01  2.4971 0.01396 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_L_F_Pasyukova_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## LSL_AgeAtDeath_days ~ Population + (1 | Batch) + (1 | Population:Line) +  
##     (1 | Line:ReplicateVial)
##    Data: filter(d_LS_L_F, Censor == "0" & Supervisor.PI == "Pasyukova")
## 
## REML criterion at convergence: 112305.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4209 -0.5713  0.0810  0.6441  3.8347 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  Line:ReplicateVial (Intercept)  39.803   6.309  
##  Population:Line    (Intercept)  24.786   4.979  
##  Batch              (Intercept)   2.699   1.643  
##  Residual                       209.826  14.485  
## Number of obs: 13520, groups:  
## Line:ReplicateVial, 1352; Population:Line, 169; Batch, 2
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   43.97758    1.68064   3.98971  26.167  1.3e-05 ***
## PopulationGI  -0.13317    1.99074 160.00824  -0.067    0.947    
## PopulationKA   2.78562    1.75818 160.00824   1.584    0.115    
## PopulationMA  -0.36000    1.75818 160.00824  -0.205    0.838    
## PopulationMU  -0.05625    1.75818 160.00824  -0.032    0.975    
## PopulationRE   0.20156    1.86975 160.00824   0.108    0.914    
## PopulationUM  -4.65347    1.80862 160.00824  -2.573    0.011 *  
## PopulationVA   0.07812    1.75818 160.00824   0.044    0.965    
## PopulationYE  -2.67250    1.75818 160.00824  -1.520    0.130    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.440                                                 
## PopulatinKA -0.498  0.421                                          
## PopulatinMA -0.498  0.421  0.476                                   
## PopulatinMU -0.498  0.421  0.476  0.476                            
## PopulatinRE -0.468  0.395  0.448  0.448  0.448                     
## PopulatinUM -0.484  0.409  0.463  0.463  0.463  0.435              
## PopulatinVA -0.498  0.421  0.476  0.476  0.476  0.448  0.463       
## PopulatinYE -0.498  0.421  0.476  0.476  0.476  0.448  0.463  0.476
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0111326 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00449959 (tol = 0.002, component 1)
anova(LS_L_M_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population   9879  1234.9     8 159.99  4.4281 7.308e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_L_M_Pasyukova_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## LSL_AgeAtDeath_days ~ Population + (1 | Batch) + (1 | Population:Line) +  
##     (1 | Line:ReplicateVial)
##    Data: filter(d_LS_L_M, Censor == "0" & Supervisor.PI == "Pasyukova")
## 
## REML criterion at convergence: 116093.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0485 -0.5537  0.0609  0.6453  2.8822 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  Line:ReplicateVial (Intercept)  49.623   7.044  
##  Population:Line    (Intercept)  31.736   5.633  
##  Batch              (Intercept)   1.325   1.151  
##  Residual                       278.870  16.699  
## Number of obs: 13520, groups:  
## Line:ReplicateVial, 1352; Population:Line, 169; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   50.4761     1.5965  11.8203  31.616 8.68e-13 ***
## PopulationGI  -2.7135     2.2515 159.9923  -1.205 0.229908    
## PopulationKA   3.0332     1.9885 159.9923   1.525 0.129145    
## PopulationMA  -2.9168     1.9885 159.9923  -1.467 0.144384    
## PopulationMU   0.1282     1.9885 159.9923   0.064 0.948683    
## PopulationRE  -2.1693     2.1147 159.9923  -1.026 0.306523    
## PopulationUM  -6.8617     2.0456 159.9923  -3.354 0.000993 ***
## PopulationVA  -1.1931     1.9885 159.9923  -0.600 0.549368    
## PopulationYE  -6.0003     1.9885 159.9934  -3.017 0.002966 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.524                                                 
## PopulatinKA -0.593  0.421                                          
## PopulatinMA -0.593  0.421  0.476                                   
## PopulatinMU -0.593  0.421  0.476  0.476                            
## PopulatinRE -0.558  0.395  0.448  0.448  0.448                     
## PopulatinUM -0.577  0.409  0.463  0.463  0.463  0.435              
## PopulatinVA -0.593  0.421  0.476  0.476  0.476  0.448  0.463       
## PopulatinYE -0.593  0.421  0.476  0.476  0.476  0.448  0.463  0.476
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00449959 (tol = 0.002, component 1)

Survival Analyses

Flatt Lab

Parsch Lab

Pasyukova Lab

coxme

#Please refer to "_SurvivalAnalyses_" folder

Lab correlations

There are 0 populations and 133 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Cold-shock mortality

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Gonzalez Lab : Llewellyn Green, Josefa Gonzalez, Miriam Merenciano

Kozeretska Lab : Svitlana Serga, Alexandra Protsenko, Oleksandr Maistrenko, Iryna Kozeretska

Vieira Lab : Jorge Vieira, Cristina P. Vieira, Pedro Duque, Tânia Dias

Data Reformatting

d_CSM <- read.csv("MasterSheets_Oct21_git/CSM_MasterSheet_Oct21.csv")
str(d_CSM)
## 'data.frame':    2584 obs. of  16 variables:
##  $ Supervisor.PI   : chr  "Gonzalez" "Gonzalez" "Gonzalez" "Gonzalez" ...
##  $ Diet            : chr  "S" "S" "S" "S" ...
##  $ Batch           : int  1 2 3 1 3 4 1 2 3 1 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : chr  "AK1" "AK1" "AK1" "AK10" ...
##  $ Sex             : chr  "M" "M" "M" "M" ...
##  $ ReplicateVialOld: int  1 2 3 1 2 3 1 2 3 1 ...
##  $ ReplicateVial   : chr  "Gonzalez_1_AK1_M_1" "Gonzalez_2_AK1_M_2" "Gonzalez_3_AK1_M_3" "Gonzalez_1_AK10_M_1" ...
##  $ Total           : int  20 19 18 20 20 20 16 20 18 16 ...
##  $ Dead            : int  14 11 3 16 4 11 8 17 4 16 ...
##  $ CSM_PropDead    : num  70 57.9 16.7 80 20 55 50 85 22.2 100 ...
##  $ CSM_PropDead_ED : num  0.7 0.579 0.167 0.8 0.2 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
d_CSM$Supervisor.PI <- as.factor(d_CSM$Supervisor.PI)
d_CSM$Diet <- as.factor(d_CSM$Diet)
d_CSM$Batch <- as.factor(d_CSM$Batch)
d_CSM$Population_Lat <- factor(d_CSM$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_CSM$Population_Lon <- factor(d_CSM$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_CSM$Population_Alt <- factor(d_CSM$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_CSM$Line <- as.factor(d_CSM$Line)
d_CSM$Sex <- as.factor(d_CSM$Sex)
d_CSM$ReplicateVial <- as.factor(d_CSM$ReplicateVial)
d_CSM$CSM_PropDead_ED <- as.numeric(d_CSM$CSM_PropDead_ED)
str(d_CSM)
## 'data.frame':    2584 obs. of  19 variables:
##  $ Supervisor.PI   : Factor w/ 3 levels "Gonzalez","Kozeretska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet            : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Batch           : Factor w/ 32 levels "1","2","3","4",..: 1 2 3 1 3 4 1 2 3 1 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : Factor w/ 172 levels "AK1","AK10","AK11",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ Sex             : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
##  $ ReplicateVialOld: int  1 2 3 1 2 3 1 2 3 1 ...
##  $ ReplicateVial   : Factor w/ 2584 levels "Gonzalez_1_AK1_F_1",..: 2 152 342 4 344 584 6 154 346 8 ...
##  $ Total           : int  20 19 18 20 20 20 16 20 18 16 ...
##  $ Dead            : int  14 11 3 16 4 11 8 17 4 16 ...
##  $ CSM_PropDead    : num  70 57.9 16.7 80 20 55 50 85 22.2 100 ...
##  $ CSM_PropDead_ED : num  0.7 0.579 0.167 0.8 0.2 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat  : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon  : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt  : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_CSM_F <-subset(d_CSM,Sex=='F')
d_CSM_M <-subset(d_CSM,Sex=='M')

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_CSM <- min(d_CSM$CSM_PropDead_ED)
max_CSM <- max(d_CSM$CSM_PropDead_ED)

y-axis is scaled by the minimum (0) and maximum (1) values in the full data set.

Gonzalez Lab

anova(CSM_F_lmer_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 0.51553 0.064441     8 147.32  1.9571 0.05577 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CSM_F_lmer_Gonzalez)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: d_CSM_F_trans[d_CSM_F_trans$Supervisor.PI == "Gonzalez", ]
## 
## REML criterion at convergence: -72
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.7096 -0.5792 -0.0380  0.6031  2.6103 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.02233  0.1494  
##  Batch           (Intercept) 0.01094  0.1046  
##  Residual                    0.03293  0.1815  
## Number of obs: 544, groups:  Line:Population, 160; Batch, 9
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    1.32326    0.05395  34.64144  24.529  < 2e-16 ***
## PopulationGI  -0.17210    0.06168 148.74549  -2.790  0.00596 ** 
## PopulationKA  -0.08811    0.06098 141.06586  -1.445  0.15070    
## PopulationMA  -0.08720    0.05703 147.11430  -1.529  0.12841    
## PopulationMU  -0.03564    0.05701 147.02810  -0.625  0.53281    
## PopulationRE  -0.11019    0.06032 144.50429  -1.827  0.06981 .  
## PopulationUM  -0.19304    0.06271 145.34606  -3.078  0.00249 ** 
## PopulationVA  -0.11783    0.05672 144.00044  -2.078  0.03953 *  
## PopulationYE  -0.06171    0.05682 143.73777  -1.086  0.27934    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.478                                                 
## PopulatinKA -0.488  0.425                                          
## PopulatinMA -0.525  0.454  0.461                                   
## PopulatinMU -0.520  0.454  0.460  0.491                            
## PopulatinRE -0.506  0.429  0.438  0.469  0.465                     
## PopulatinUM -0.474  0.413  0.418  0.448  0.446  0.424              
## PopulatinVA -0.517  0.456  0.460  0.491  0.493  0.464  0.447       
## PopulatinYE -0.533  0.456  0.466  0.498  0.494  0.476  0.451  0.492
anova(CSM_M_lmer_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 0.66328 0.082911     8 147.95  1.7495 0.09162 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CSM_M_lmer_Gonzalez)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: d_CSM_M_trans[d_CSM_M_trans$Supervisor.PI == "Gonzalez", ]
## 
## REML criterion at convergence: 107.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2902 -0.5700 -0.0374  0.4998  2.9573 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.02578  0.1606  
##  Batch           (Intercept) 0.03675  0.1917  
##  Residual                    0.04739  0.2177  
## Number of obs: 544, groups:  Line:Population, 160; Batch, 9
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    0.890522   0.078853  16.836637  11.293 2.82e-09 ***
## PopulationGI   0.074983   0.068797 149.870804   1.090   0.2775    
## PopulationKA   0.098935   0.067867 140.622954   1.458   0.1471    
## PopulationMA   0.026821   0.063580 147.725741   0.422   0.6737    
## PopulationMU   0.009290   0.063549 147.617182   0.146   0.8840    
## PopulationRE   0.004401   0.067228 144.500011   0.065   0.9479    
## PopulationUM  -0.057059   0.069875 145.388654  -0.817   0.4155    
## PopulationVA  -0.020614   0.063173 144.041366  -0.326   0.7447    
## PopulationYE   0.145985   0.063307 143.509589   2.306   0.0225 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.363                                                 
## PopulatinKA -0.372  0.424                                          
## PopulatinMA -0.400  0.453  0.461                                   
## PopulatinMU -0.396  0.452  0.459  0.490                            
## PopulatinRE -0.387  0.428  0.438  0.468  0.464                     
## PopulatinUM -0.361  0.411  0.418  0.447  0.445  0.423              
## PopulatinVA -0.393  0.454  0.460  0.490  0.492  0.463  0.446       
## PopulatinYE -0.407  0.455  0.466  0.498  0.493  0.477  0.450  0.491

Kozeretska Lab

anova(CSM_F_lmer_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Population 0.41646 0.052057     8 113.84  0.4214 0.9061
summary(CSM_F_lmer_Kozeretska)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: d_CSM_F_trans[d_CSM_F_trans$Supervisor.PI == "Kozeretska", ]
## 
## REML criterion at convergence: 251.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.98016 -0.61640 -0.09206  0.58901  2.29545 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.02928  0.1711  
##  Batch           (Intercept) 0.03133  0.1770  
##  Residual                    0.12353  0.3515  
## Number of obs: 244, groups:  Line:Population, 130; Batch, 2
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)  
## (Intercept)    0.88456    0.14574   1.60370   6.069   0.0429 *
## PopulationGI  -0.07755    0.12814 120.55726  -0.605   0.5462  
## PopulationKA  -0.13366    0.11571 114.94299  -1.155   0.2505  
## PopulationMA  -0.12261    0.10605 120.05564  -1.156   0.2499  
## PopulationMU  -0.07069    0.10447 117.01418  -0.677   0.5000  
## PopulationRE  -0.10789    0.11427 108.41666  -0.944   0.3472  
## PopulationUM  -0.18809    0.11681 110.11572  -1.610   0.1102  
## PopulationVA  -0.06807    0.10038 112.54488  -0.678   0.4990  
## PopulationYE  -0.06874    0.11236 123.47720  -0.612   0.5418  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.288                                                 
## PopulatinKA -0.329  0.362                                          
## PopulatinMA -0.337  0.386  0.425                                   
## PopulatinMU -0.341  0.392  0.430  0.477                            
## PopulatinRE -0.324  0.363  0.408  0.433  0.439                     
## PopulatinUM -0.306  0.351  0.386  0.427  0.433  0.393              
## PopulatinVA -0.349  0.406  0.441  0.498  0.507  0.454  0.452       
## PopulatinYE -0.321  0.366  0.404  0.443  0.449  0.410  0.402  0.468
anova(CSM_M_lmer_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population 0.87614 0.10952     8 112.87  0.8066 0.5981
summary(CSM_M_lmer_Kozeretska)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: d_CSM_M_trans[d_CSM_M_trans$Supervisor.PI == "Kozeretska", ]
## 
## REML criterion at convergence: 238.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.68234 -0.75897  0.05052  0.69572  1.83245 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.005378 0.07333 
##  Batch           (Intercept) 0.034408 0.18549 
##  Residual                    0.135783 0.36849 
## Number of obs: 244, groups:  Line:Population, 130; Batch, 2
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    1.141197   0.147492   1.437570   7.737   0.0382 *
## PopulationGI   0.009018   0.115920 121.640216   0.078   0.9381  
## PopulationKA  -0.142703   0.104274 114.986203  -1.369   0.1738  
## PopulationMA  -0.049584   0.095842 118.570191  -0.517   0.6059  
## PopulationMU  -0.127495   0.094278 117.572178  -1.352   0.1789  
## PopulationRE  -0.088661   0.102357 103.311622  -0.866   0.3884  
## PopulationUM  -0.130669   0.104837 107.557830  -1.246   0.2153  
## PopulationVA  -0.084597   0.090287 111.769032  -0.937   0.3508  
## PopulationYE   0.050031   0.101826 124.595934   0.491   0.6241  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.256                                                 
## PopulatinKA -0.295  0.361                                          
## PopulatinMA -0.300  0.385  0.424                                   
## PopulatinMU -0.304  0.391  0.431  0.478                            
## PopulatinRE -0.291  0.365  0.411  0.436  0.443                     
## PopulatinUM -0.275  0.353  0.389  0.429  0.436  0.399              
## PopulatinVA -0.312  0.407  0.442  0.501  0.510  0.459  0.457       
## PopulatinYE -0.285  0.364  0.403  0.441  0.449  0.412  0.403  0.469

Vieira Lab

anova(CSM_F_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Population 0.37712 0.047141     8 28.474  1.5683 0.1786
summary(CSM_F_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: d_CSM_F_trans[d_CSM_F_trans$Supervisor.PI == "Vieira", ]
## 
## REML criterion at convergence: -63.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.3746 -0.5691 -0.0185  0.6048  2.3916 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.030988 0.17604 
##  Batch           (Intercept) 0.002363 0.04861 
##  Residual                    0.030059 0.17337 
## Number of obs: 504, groups:  Line:Population, 168; Batch, 32
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   1.268575   0.049136 40.512400  25.818   <2e-16 ***
## PopulationGI -0.035655   0.076006 92.151141  -0.469   0.6401    
## PopulationKA  0.058282   0.070095 34.031699   0.831   0.4115    
## PopulationMA -0.002099   0.070558 28.455908  -0.030   0.9765    
## PopulationMU  0.004964   0.067787 89.243620   0.073   0.9418    
## PopulationRE -0.022527   0.075564 23.529537  -0.298   0.7682    
## PopulationUM -0.111670   0.069922 50.602527  -1.597   0.1165    
## PopulationVA  0.122112   0.071029 26.096556   1.719   0.0974 .  
## PopulationYE  0.016747   0.071084 25.536015   0.236   0.8156    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.613                                                 
## PopulatinKA -0.701  0.430                                          
## PopulatinMA -0.696  0.427  0.492                                   
## PopulatinMU -0.667  0.431  0.468  0.465                            
## PopulatinRE -0.650  0.399  0.462  0.453  0.434                     
## PopulatinUM -0.690  0.433  0.503  0.481  0.483  0.449              
## PopulatinVA -0.692  0.424  0.485  0.482  0.461  0.450  0.477       
## PopulatinYE -0.691  0.424  0.484  0.487  0.461  0.449  0.477  0.523
anova(CSM_M_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 0.72875 0.091094     8 52.099  2.4479 0.02494 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CSM_M_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: d_CSM_M_trans[d_CSM_M_trans$Supervisor.PI == "Vieira", ]
## 
## REML criterion at convergence: 86.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.11405 -0.49749 -0.01356  0.56781  2.06627 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.051485 0.22690 
##  Batch           (Intercept) 0.009326 0.09657 
##  Residual                    0.037214 0.19291 
## Number of obs: 504, groups:  Line:Population, 168; Batch, 32
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    1.04462    0.06727  63.64605  15.528  < 2e-16 ***
## PopulationGI   0.04585    0.10030 124.28802   0.457  0.64837    
## PopulationKA   0.06970    0.09659  58.91769   0.722  0.47342    
## PopulationMA   0.01715    0.09829  47.90436   0.175  0.86220    
## PopulationMU   0.23817    0.08926 133.67896   2.668  0.00856 ** 
## PopulationRE   0.05815    0.10633  40.87319   0.547  0.58744    
## PopulationUM  -0.02656    0.09452  83.92641  -0.281  0.77938    
## PopulationVA   0.09561    0.09922  46.76312   0.964  0.34018    
## PopulationYE   0.28626    0.09937  46.78454   2.881  0.00597 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.593                                                 
## PopulatinKA -0.694  0.413                                          
## PopulatinMA -0.684  0.406  0.486                                   
## PopulatinMU -0.626  0.417  0.438  0.428                            
## PopulatinRE -0.633  0.375  0.456  0.433  0.396                     
## PopulatinUM -0.680  0.426  0.514  0.466  0.474  0.432              
## PopulatinVA -0.678  0.402  0.471  0.467  0.424  0.429  0.461       
## PopulatinYE -0.677  0.401  0.470  0.478  0.424  0.428  0.460  0.557

Lab correlations

There are 9 populations and 126 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Chill-coma recovery time

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Mensh Lab : Florencia Putero, Lucas Kreiman, Julian Mensh

Vieira Lab : Jorge Vieira, Cristina P. Vieira, Pedro Duque, Tânia Dias

Data Reformatting

d_CCRT <- read.csv("MasterSheets_Oct21_git/CCRT_MasterSheet_Oct21.csv")
str(d_CCRT)
## 'data.frame':    2016 obs. of  15 variables:
##  $ Supervisor.PI   : chr  "Vieira" "Vieira" "Vieira" "Vieira" ...
##  $ Diet            : chr  "S" "S" "S" "S" ...
##  $ Batch           : int  4 4 4 4 4 4 4 4 4 4 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Sex             : chr  "F" "F" "F" "F" ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : chr  "Vieira_4_AK1_F_1" "Vieira_4_AK1_F_1" "Vieira_4_AK1_F_1" "Vieira_4_AK1_F_1" ...
##  $ Individual      : int  1 2 3 4 5 6 1 2 3 4 ...
##  $ CCRT_seconds    : int  2381 1902 1847 1640 2202 1444 1550 1900 1505 1524 ...
##  $ Censor          : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
d_CCRT$Supervisor.PI <- as.factor(d_CCRT$Supervisor.PI)
d_CCRT$Diet <- as.factor(d_CCRT$Diet)
d_CCRT$Batch <- as.factor(d_CCRT$Batch)
d_CCRT$Population_Lat <- factor(d_CCRT$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_CCRT$Population_Lon <- factor(d_CCRT$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_CCRT$Population_Alt <- factor(d_CCRT$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_CCRT$Line <- as.factor(d_CCRT$Line)
d_CCRT$Sex <- as.factor(d_CCRT$Sex)
d_CCRT$ReplicateVial <- as.factor(d_CCRT$ReplicateVial)
d_CCRT$CCRT_seconds <- as.numeric(d_CCRT$CCRT_seconds)
d_CCRT$Censor <- as.numeric(d_CCRT$Censor)
str(d_CCRT)
## 'data.frame':    2016 obs. of  18 variables:
##  $ Supervisor.PI   : Factor w/ 1 level "Vieira": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet            : Factor w/ 1 level "S": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch           : Factor w/ 27 levels "1","2","3","4",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : Factor w/ 168 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex             : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 2 2 2 2 ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : Factor w/ 336 levels "Vieira_1_UM13_F_1",..: 235 235 235 235 235 235 236 236 236 236 ...
##  $ Individual      : int  1 2 3 4 5 6 1 2 3 4 ...
##  $ CCRT_seconds    : num  2381 1902 1847 1640 2202 ...
##  $ Censor          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat  : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon  : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt  : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_CCRT_F <-subset(d_CCRT,Sex=='F')
d_CCRT_M <-subset(d_CCRT,Sex=='M')

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_CCRT <- min(d_CCRT$CCRT_seconds)
max_CCRT <- max(d_CCRT$CCRT_seconds)

y-axis is scaled by the minimum (0) and maximum (1) values in the full data set.

Vieira Lab

## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(CCRT_F_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population 5461681  682710     8 39.995  3.7236 0.002453 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CCRT_F_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CCRT_seconds ~ Population + (1 | Population:Line) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: filter(d_CCRT_F, Censor == "0" & Supervisor.PI == "Vieira")
## 
## REML criterion at convergence: 14874.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1461 -0.5789 -0.1437  0.3652  5.0319 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  ReplicateVial:Line (Intercept)    799.8  28.28  
##  Population:Line    (Intercept)  29469.4 171.67  
##  Batch              (Intercept)   3852.3  62.07  
##  Residual                       183347.9 428.19  
## Number of obs: 993, groups:  
## ReplicateVial:Line, 168; Population:Line, 168; Batch, 27
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   1582.02      62.96   38.07  25.126  < 2e-16 ***
## PopulationGI   -55.11      90.30  158.62  -0.610  0.54250    
## PopulationKA  -193.88      88.41   40.42  -2.193  0.03413 *  
## PopulationMA  -287.19      89.91   30.12  -3.194  0.00328 ** 
## PopulationMU   -49.61      91.16   26.95  -0.544  0.59079    
## PopulationRE  -147.82      88.37  115.37  -1.673  0.09711 .  
## PopulationUM   131.98      89.75   41.13   1.470  0.14905    
## PopulationVA  -180.46      89.51   33.30  -2.016  0.05193 .  
## PopulationYE  -100.02      88.28   37.67  -1.133  0.26438    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.573                                                 
## PopulatinKA -0.712  0.408                                          
## PopulatinMA -0.700  0.401  0.508                                   
## PopulatinMU -0.686  0.416  0.489  0.481                            
## PopulatinRE -0.656  0.391  0.468  0.459  0.451                     
## PopulatinUM -0.698  0.405  0.515  0.489  0.488  0.485              
## PopulatinVA -0.703  0.403  0.501  0.493  0.483  0.461  0.491       
## PopulatinYE -0.713  0.408  0.508  0.499  0.489  0.468  0.498  0.573
#could simplify the model, as some random factors explain very little
## boundary (singular) fit: see ?isSingular
## Warning: Model failed to converge with 1 negative eigenvalue: -4.2e-05
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(CCRT_M_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 6454279  806785     8 159.24   5.132 1.058e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CCRT_M_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CCRT_seconds ~ Population + (1 | Population:Line) + (1 | Batch) +  
##     (1 | ReplicateVial:Line)
##    Data: filter(d_CCRT_M, Censor == "0" & Supervisor.PI == "Vieira")
## 
## REML criterion at convergence: 14768.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3458 -0.5562 -0.1358  0.3737  4.4516 
## 
## Random effects:
##  Groups             Name        Variance  Std.Dev. 
##  ReplicateVial:Line (Intercept) 4.448e+04 2.109e+02
##  Population:Line    (Intercept) 1.539e+03 3.923e+01
##  Batch              (Intercept) 8.020e-05 8.955e-03
##  Residual                       1.572e+05 3.965e+02
## Number of obs: 993, groups:  
## ReplicateVial:Line, 168; Population:Line, 168; Batch, 27
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  1563.5083    60.0926  157.5243  26.018  < 2e-16 ***
## PopulationGI  281.3506    95.7428  157.5244   2.939  0.00379 ** 
## PopulationKA -117.8561    85.2747  159.5721  -1.382  0.16888    
## PopulationMA -233.8839    85.0558  158.0250  -2.750  0.00666 ** 
## PopulationMU  130.9000    84.9838  157.5242   1.540  0.12550    
## PopulationRE    0.1418    90.2451  158.2211   0.002  0.99875    
## PopulationUM   14.8551    86.2529  158.6143   0.172  0.86348    
## PopulationVA  -58.1077    85.1283  158.5331  -0.683  0.49586    
## PopulationYE   89.6352    85.3485  160.1033   1.050  0.29520    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.628                                                 
## PopulatinKA -0.705  0.442                                          
## PopulatinMA -0.707  0.443  0.498                                   
## PopulatinMU -0.707  0.444  0.498  0.500                            
## PopulatinRE -0.666  0.418  0.469  0.470  0.471                     
## PopulatinUM -0.697  0.437  0.491  0.492  0.493  0.464              
## PopulatinVA -0.706  0.443  0.497  0.499  0.499  0.470  0.492       
## PopulatinYE -0.704  0.442  0.496  0.497  0.498  0.469  0.491  0.497
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
#model needs simplification

Mensh Lab

Survival Analyses

Vieira Lab

coxme

#Please refer to "_SurvivalAnalyses_" folder

Lab correlations

NA

Heat-shock mortality

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Parsch Lab : Eliza Argyridou, Amanda Glaser-Schmitt, John Parsch

Vieira Lab : Jorge Vieira, Cristina P. Vieira, Pedro Duque, Tânia Dias

Data Reformatting

d_HSM <- read.csv("MasterSheets_Oct21_git/HSM_MasterSheet_Oct21.csv")
str(d_HSM)
## 'data.frame':    31244 obs. of  15 variables:
##  $ Supervisor.PI   : chr  "Vieira" "Vieira" "Vieira" "Vieira" ...
##  $ Diet            : chr  "S" "S" "S" "S" ...
##  $ Batch           : int  5 5 5 5 5 5 5 5 5 5 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Sex             : chr  "F" "F" "F" "F" ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : chr  "Vieira_5_AK1_F_1" "Vieira_5_AK1_F_1" "Vieira_5_AK1_F_1" "Vieira_5_AK1_F_1" ...
##  $ Individual      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ TimeDeath_min   : int  240 240 300 300 300 330 360 360 360 390 ...
##  $ Censor          : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
d_HSM$Supervisor.PI <- as.factor(d_HSM$Supervisor.PI)
d_HSM$Diet <- as.factor(d_HSM$Diet)
d_HSM$Batch <- as.factor(d_HSM$Batch)
d_HSM$Population_Lat <- factor(d_HSM$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_HSM$Population_Lon <- factor(d_HSM$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_HSM$Population_Alt <- factor(d_HSM$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_HSM$Line <- as.factor(d_HSM$Line)
d_HSM$Sex <- as.factor(d_HSM$Sex)
d_HSM$ReplicateVial <- as.factor(d_HSM$ReplicateVial)
d_HSM$TimeDeath_min <- as.numeric(d_HSM$TimeDeath_min)
d_HSM$Censor <- as.numeric(d_HSM$Censor)
str(d_HSM)
## 'data.frame':    31244 obs. of  18 variables:
##  $ Supervisor.PI   : Factor w/ 2 levels "Parsch","Vieira": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Diet            : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Batch           : Factor w/ 32 levels "1","2","3","4",..: 5 5 5 5 5 5 5 5 5 5 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : Factor w/ 171 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex             : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : Factor w/ 1816 levels "Parsch_1_AK1_F_1",..: 1691 1691 1691 1691 1691 1691 1691 1691 1691 1691 ...
##  $ Individual      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ TimeDeath_min   : num  240 240 300 300 300 330 360 360 360 390 ...
##  $ Censor          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat  : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon  : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt  : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_HSM_F <-subset(d_HSM,Sex=='F')
d_HSM_M <-subset(d_HSM,Sex=='M')

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

min_HSM <- min(d_HSM$TimeDeath_min)
max_HSM <- max(d_HSM$TimeDeath_min)

y-axis is scaled by the minimum (30) and maximum (505) values in the full data set.

Parsch Lab

anova(HSM_F_lmer_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 348447   43556     8 141.62  7.2493 4.868e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(HSM_F_lmer_Parsch)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TimeDeath_min ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: filter(d_HSM_F, Censor == "0" & Supervisor.PI == "Parsch")
## 
## REML criterion at convergence: 39308.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.9904 -0.5935  0.1317  0.6680  2.9181 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 2411     49.11   
##  Batch           (Intercept) 3662     60.51   
##  Residual                    6008     77.51   
## Number of obs: 3384, groups:  Line:Population, 133; Batch, 9
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  394.1080    25.0877  17.5868  15.709 8.64e-12 ***
## PopulationGI -56.6034    20.0704 136.3060  -2.820 0.005515 ** 
## PopulationKA -24.4190    20.1169 136.0422  -1.214 0.226906    
## PopulationMA  27.8126    20.7791 142.1194   1.338 0.182873    
## PopulationMU   6.0355    19.9796 131.8722   0.302 0.763065    
## PopulationRE -70.8761    20.6894 149.0449  -3.426 0.000792 ***
## PopulationUM   0.9422    22.0588 180.8989   0.043 0.965978    
## PopulationVA  -9.1233    22.1012 183.0276  -0.413 0.680238    
## PopulationYE -81.9769    20.3008 140.3281  -4.038 8.83e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.428                                                 
## PopulatinKA -0.426  0.554                                          
## PopulatinMA -0.408  0.493  0.501                                   
## PopulatinMU -0.423  0.511  0.518  0.523                            
## PopulatinRE -0.445  0.519  0.518  0.498  0.515                     
## PopulatinUM -0.427  0.494  0.490  0.465  0.481  0.579              
## PopulatinVA -0.426  0.492  0.488  0.464  0.480  0.577  0.572       
## PopulatinYE -0.448  0.534  0.529  0.503  0.522  0.576  0.561  0.559
anova(HSM_M_lmer_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 319547   39943     8 134.26  8.2397 4.875e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(HSM_M_lmer_Parsch)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TimeDeath_min ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: filter(d_HSM_M, Censor == "0" & Supervisor.PI == "Parsch")
## 
## REML criterion at convergence: 57794.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.2106 -0.6839  0.0461  0.6733  2.8035 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 4376     66.15   
##  Batch           (Intercept) 3268     57.17   
##  Residual                    4848     69.63   
## Number of obs: 5065, groups:  Line:Population, 135; Batch, 9
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   382.380     25.995   25.149  14.710 7.34e-14 ***
## PopulationGI  -97.318     24.844  131.718  -3.917 0.000143 ***
## PopulationKA  -42.710     24.746  129.653  -1.726 0.086736 .  
## PopulationMA    5.031     24.687  128.410   0.204 0.838830    
## PopulationMU  -30.094     24.625  127.191  -1.222 0.223939    
## PopulationRE -119.245     25.215  139.083  -4.729 5.47e-06 ***
## PopulationUM  -13.127     25.684  149.157  -0.511 0.610032    
## PopulationVA  -29.298     25.675  148.962  -1.141 0.255655    
## PopulationYE -124.398     24.964  133.987  -4.983 1.90e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.478                                                 
## PopulatinKA -0.478  0.508                                          
## PopulatinMA -0.474  0.492  0.497                                   
## PopulatinMU -0.476  0.493  0.498  0.504                            
## PopulatinRE -0.487  0.494  0.494  0.490  0.492                     
## PopulatinUM -0.484  0.488  0.487  0.481  0.482  0.533              
## PopulatinVA -0.484  0.488  0.487  0.481  0.482  0.533  0.540       
## PopulatinYE -0.488  0.500  0.499  0.493  0.494  0.521  0.522  0.522

p_HSM_M_Parsch <- ggplot(data = filter(d_HSM_M, (Censor == "0") & (Supervisor.PI == "Parsch")), aes(x=Population_Lat, y=TimeDeath_min, fill=Batch)) + 
        geom_boxplot(outlier.shape = NA, notch=FALSE) + 
        labs(title="p_HSM_M_Parsch", x="Population", y = "TimeDeath_min") + ylim(c(min_HSM, max_HSM))+ droseu_theme
p_HSM_M_Parsch

pdf(file="HeatShock/p_HSM_M_Parsch.pdf",width=8, height=5)
p_HSM_M_Parsch
invisible(dev.off())

Vieira Lab

anova(HSM_F_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## Population 167083   20885     8 51.73  6.8529 4.169e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(HSM_F_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TimeDeath_min ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: filter(d_HSM_F, Censor == "0" & Supervisor.PI == "Vieira")
## 
## REML criterion at convergence: 57881.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.2647 -0.5129  0.1397  0.7262  2.3668 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  433.75  20.827  
##  Batch           (Intercept)   17.84   4.223  
##  Residual                    3047.69  55.206  
## Number of obs: 5310, groups:  Line:Population, 168; Batch, 32
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  362.7389     5.7290  72.7338  63.316  < 2e-16 ***
## PopulationGI -28.7426     8.5826 111.6208  -3.349 0.001107 ** 
## PopulationKA   1.1336     8.3189  72.8507   0.136 0.891986    
## PopulationMA   9.7117     8.1535  54.1027   1.191 0.238812    
## PopulationMU -30.8386     7.6764 111.6067  -4.017 0.000107 ***
## PopulationRE  14.4083     8.7199  49.0669   1.652 0.104849    
## PopulationUM   1.8238     8.1387  80.5809   0.224 0.823259    
## PopulationVA  -5.3199     7.9537  45.2138  -0.669 0.506988    
## PopulationYE  -0.5634     8.0726  49.4488  -0.070 0.944641    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.649                                                 
## PopulatinKA -0.688  0.447                                          
## PopulatinMA -0.703  0.456  0.486                                   
## PopulatinMU -0.711  0.475  0.490  0.499                            
## PopulatinRE -0.657  0.427  0.456  0.462  0.467                     
## PopulatinUM -0.698  0.459  0.496  0.491  0.507  0.459              
## PopulatinVA -0.720  0.468  0.496  0.506  0.512  0.473  0.503       
## PopulatinYE -0.710  0.461  0.489  0.502  0.504  0.466  0.495  0.540
anova(HSM_M_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population  83890   10486     8 53.384  3.5201 0.002454 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(HSM_M_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TimeDeath_min ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: filter(d_HSM_M, Censor == "0" & Supervisor.PI == "Vieira")
## 
## REML criterion at convergence: 95195.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.8393 -0.6019  0.0454  0.6858  2.7930 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 1002.0   31.65   
##  Batch           (Intercept)  260.4   16.14   
##  Residual                    2979.0   54.58   
## Number of obs: 8743, groups:  Line:Population, 168; Batch, 32
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   343.597      9.407  61.170  36.527  < 2e-16 ***
## PopulationGI  -66.412     13.504 130.651  -4.918 2.58e-06 ***
## PopulationKA  -16.810     13.520  60.216  -1.243  0.21856    
## PopulationMA  -23.992     13.906  46.304  -1.725  0.09114 .  
## PopulationMU  -37.395     11.948 145.444  -3.130  0.00211 ** 
## PopulationRE  -16.376     15.164  39.338  -1.080  0.28674    
## PopulationUM  -29.091     12.987  89.371  -2.240  0.02757 *  
## PopulationVA  -23.582     14.048  45.588  -1.679  0.10007    
## PopulationYE  -18.602     14.066  47.091  -1.322  0.19239    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.575                                                 
## PopulatinKA -0.690  0.400                                          
## PopulatinMA -0.676  0.389  0.485                                   
## PopulatinMU -0.595  0.410  0.419  0.403                            
## PopulatinRE -0.620  0.357  0.455  0.420  0.370                     
## PopulatinUM -0.672  0.420  0.526  0.456  0.470  0.420              
## PopulatinVA -0.670  0.385  0.462  0.459  0.399  0.415  0.450       
## PopulatinYE -0.669  0.385  0.462  0.477  0.398  0.415  0.449  0.591

Survival Analyses

Parsch Lab

Vieira Lab

coxme

#Please refer to "_SurvivalAnalyses_" folder

Lab correlations

There are 9 populations and 132 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Diapause

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Bergland Lab : Liam Miller, Alan Bergland, Priscilla Erickson

Flatt Lab : Esra Durmaz, Envel Kerdaffrec, Thibault Schowing, Virginie Thieu, Marisa Rodrigues, Thomas Flatt

Schlotterer Lab : Manolis Lyrakis, Christian Schlötterer

Data Reformatting

d_Dia <- read.csv("MasterSheets_Oct21_git/DIA_MasterSheet_Oct21.csv")
str(d_Dia)
## 'data.frame':    8206 obs. of  12 variables:
##  $ Supervisor.PI    : chr  "Bergland" "Bergland" "Bergland" "Bergland" ...
##  $ Diet             : chr  "S" "S" "S" "S" ...
##  $ Batch            : int  3 3 3 3 3 3 3 3 3 3 ...
##  $ Population       : chr  "AK" "AK" "AK" "AK" ...
##  $ Line             : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Individual       : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ MostAdvancedStage: int  9 7 10 8 8 8 8 9 9 8 ...
##  $ NumberOfEggs     : int  1 0 3 0 1 3 0 4 3 1 ...
##  $ Country          : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude         : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude        : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude         : int  88 88 88 88 88 88 88 88 88 88 ...
d_Dia <- d_Dia %>%
  mutate(Supervisor.PI = as.factor(Supervisor.PI),
         Diet = as.factor(Diet),
         Batch = as.factor(Batch),
         Population = as.factor(Population),
         Line = as.factor(Line),
         MostAdvancedStage = as.numeric(MostAdvancedStage),
         NumberOfEggs = as.numeric(NumberOfEggs),
         Max_Stage7 = ifelse(MostAdvancedStage <= 7 & NumberOfEggs == 0, 1, 0),
         Max_Stage8 = ifelse(MostAdvancedStage <= 8 & NumberOfEggs == 0, 1, 0),
         Max_Stage9 = ifelse(MostAdvancedStage <= 9 & NumberOfEggs == 0, 1, 0),
         Population_Lat = factor(Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK")),
         Population_Lon = factor(Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA")),
         Population_Alt = factor(Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE")),
         Latitude = as.numeric(Latitude),
         Longitude = as.numeric(Longitude),
         Altitude = as.numeric(Altitude))
str(d_Dia)
## 'data.frame':    8206 obs. of  18 variables:
##  $ Supervisor.PI    : Factor w/ 3 levels "Bergland","Flatt",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet             : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Batch            : Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 3 3 3 3 ...
##  $ Population       : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Line             : Factor w/ 168 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Individual       : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ MostAdvancedStage: num  9 7 10 8 8 8 8 9 9 8 ...
##  $ NumberOfEggs     : num  1 0 3 0 1 3 0 4 3 1 ...
##  $ Country          : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude         : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude        : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude         : num  88 88 88 88 88 88 88 88 88 88 ...
##  $ Max_Stage7       : num  0 1 0 0 0 0 0 0 0 0 ...
##  $ Max_Stage8       : num  0 1 0 1 0 0 1 0 0 0 ...
##  $ Max_Stage9       : num  0 1 0 1 0 0 1 0 0 0 ...
##  $ Population_Lat   : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon   : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt   : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...

Descriptive Statistics

##### calculate diapause proportions per line, with and without batch
d_Dia_Line_wbatch <- d_Dia %>%
  group_by(Supervisor.PI, Batch, Population, Population_Lat, Population_Lon, Population_Alt, Line) %>%
  dplyr::summarise(n_ind = as.numeric(n()),
            Max_Stage7_Prop = mean(Max_Stage7, na.rm = T),
            Max_Stage8_Prop = mean(Max_Stage8, na.rm = T),
            Max_Stage9_Prop = mean(Max_Stage9, na.rm = T),
            Max_Stage7_Prop_Asin = asin(sqrt(mean(Max_Stage7, na.rm = T))),
            Max_Stage8_Prop_Asin = asin(sqrt(mean(Max_Stage8, na.rm = T))),
            Max_Stage9_Prop_Asin = asin(sqrt(mean(Max_Stage9, na.rm = T))),
            .groups = "drop")
 
d_Dia_Line_wobatch <- d_Dia %>%
  group_by(Supervisor.PI, Population, Population_Lat, Population_Lon, Population_Alt, Line) %>%
  dplyr::summarise(n_ind = as.numeric(n()),
            Max_Stage7_Prop = mean(Max_Stage7, na.rm = T),
            Max_Stage8_Prop = mean(Max_Stage8, na.rm = T),
            Max_Stage9_Prop = mean(Max_Stage9, na.rm = T),
            Max_Stage7_Prop_Asin = asin(sqrt(mean(Max_Stage7, na.rm = T))),
            Max_Stage8_Prop_Asin = asin(sqrt(mean(Max_Stage8, na.rm = T))),
            Max_Stage9_Prop_Asin = asin(sqrt(mean(Max_Stage9, na.rm = T))),
            .groups = "drop")

Descriptive statistics at the line level for Max Stage 9, with batch information :

Descriptive statistics at the line level for Max Stage 9, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

## Joining, by = "Population"

Bergland Lab

## boundary (singular) fit: see ?isSingular
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(Dia_lmer_Bergland)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 1.3521 0.16901     8 142.78  2.4502 0.01627 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Dia_lmer_Bergland)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Max_Stage9_Prop ~ Population + (1 | Population:Line) + (1 | Batch)
##    Data: d_Dia_trans %>% filter(Supervisor.PI == "Bergland")
## 
## REML criterion at convergence: 120.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.65951 -0.55229  0.06555  0.60594  2.16115 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Population:Line (Intercept) 0.03207  0.1791  
##  Batch           (Intercept) 0.00000  0.0000  
##  Residual                    0.06898  0.2626  
## Number of obs: 189, groups:  Population:Line, 158; Batch, 3
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    0.83272    0.07493 171.53702  11.114  < 2e-16 ***
## PopulationGI   0.01514    0.10952 162.37619   0.138  0.89025    
## PopulationKA   0.08145    0.10052 153.94814   0.810  0.41902    
## PopulationMA   0.19121    0.10348 165.25148   1.848  0.06643 .  
## PopulationMU   0.19054    0.10267 150.73567   1.856  0.06544 .  
## PopulationRE   0.03330    0.11147 161.33108   0.299  0.76552    
## PopulationUM   0.30637    0.10671 144.03589   2.871  0.00471 ** 
## PopulationVA   0.29892    0.10285 161.97413   2.906  0.00417 ** 
## PopulationYE   0.18925    0.10138 159.76296   1.867  0.06376 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.684                                                 
## PopulatinKA -0.745  0.510                                          
## PopulatinMA -0.724  0.495  0.540                                   
## PopulatinMU -0.730  0.499  0.544  0.528                            
## PopulatinRE -0.672  0.460  0.501  0.487  0.491                     
## PopulatinUM -0.702  0.480  0.523  0.508  0.512  0.472              
## PopulatinVA -0.728  0.498  0.543  0.527  0.532  0.490  0.512       
## PopulatinYE -0.739  0.506  0.551  0.535  0.539  0.497  0.519  0.538
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
min_Dia <- min(d_Dia_Line_wbatch$Max_Stage9_Prop)
max_Dia <- max(d_Dia_Line_wbatch$Max_Stage9_Prop)

## Joining, by = "Population"

Flatt Lab

anova(Dia_lm_Flatt)
## Analysis of Variance Table
## 
## Response: Max_Stage9_Prop
##             Df Sum Sq  Mean Sq F value Pr(>F)
## Population   8  0.202 0.025252  0.7367  0.659
## Residuals  154  5.279 0.034279
summary(Dia_lm_Flatt)
## 
## Call:
## lm(formula = Max_Stage9_Prop ~ Population, data = d_Dia_trans %>% 
##     filter(Supervisor.PI == "Flatt"))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.67844 -0.14098  0.07585  0.13055  0.15248 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.431116   0.041400  34.568   <2e-16 ***
## PopulationGI  0.009134   0.064517   0.142    0.888    
## PopulationKA  0.063826   0.058548   1.090    0.277    
## PopulationMA  0.002850   0.059314   0.048    0.962    
## PopulationMU  0.077286   0.058548   1.320    0.189    
## PopulationRE  0.019845   0.064517   0.308    0.759    
## PopulationUM  0.090335   0.062100   1.455    0.148    
## PopulationVA -0.012795   0.058548  -0.219    0.827    
## PopulationYE  0.027647   0.058548   0.472    0.637    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1851 on 154 degrees of freedom
## Multiple R-squared:  0.03686,    Adjusted R-squared:  -0.01318 
## F-statistic: 0.7367 on 8 and 154 DF,  p-value: 0.659
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons

Schlotterer Lab

anova(Dia_lmer_Schlotterer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)  
## Population 0.8957 0.11196     8 150.12  2.3476 0.0209 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Dia_lmer_Schlotterer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Max_Stage9_Prop ~ Population + (1 | Population:Line) + (1 | Batch)
##    Data: d_Dia_trans %>% filter(Supervisor.PI == "Schlotterer")
## 
## REML criterion at convergence: 122.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.11795 -0.50145 -0.00727  0.51676  1.62781 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Population:Line (Intercept) 0.05690  0.23854 
##  Batch           (Intercept) 0.00464  0.06812 
##  Residual                    0.04769  0.21839 
## Number of obs: 195, groups:  Population:Line, 159; Batch, 3
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    0.81061    0.08059  19.57204  10.059 3.56e-09 ***
## PopulationGI   0.14593    0.10778 147.49028   1.354   0.1778    
## PopulationKA   0.21820    0.09981 149.17906   2.186   0.0304 *  
## PopulationMA   0.22945    0.10034 144.10264   2.287   0.0237 *  
## PopulationMU   0.24726    0.09999 149.56005   2.473   0.0145 *  
## PopulationRE   0.21480    0.10889 155.57976   1.973   0.0503 .  
## PopulationUM   0.45234    0.10879 153.63777   4.158 5.32e-05 ***
## PopulationVA   0.25405    0.10645 152.79899   2.387   0.0182 *  
## PopulationYE   0.18575    0.10086 142.57733   1.842   0.0676 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.570                                                 
## PopulatinKA -0.615  0.462                                          
## PopulatinMA -0.612  0.458  0.494                                   
## PopulatinMU -0.615  0.460  0.496  0.495                            
## PopulatinRE -0.566  0.426  0.457  0.455  0.459                     
## PopulatinUM -0.564  0.415  0.453  0.454  0.456  0.416              
## PopulatinVA -0.578  0.432  0.466  0.464  0.468  0.430  0.428       
## PopulatinYE -0.612  0.461  0.494  0.493  0.499  0.461  0.452  0.467
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons

Lab correlations

There are 9 populations and 146 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Circadian eclosion timing

For a detailed description of tables, plots, linear models and outputs, please refer here 

#Note that the trait has been phenotyped as mixed sex.

Contributors

Wegener Lab : Susanne Klühspies, Christian Wegener

Data Reformatting

d_CET <- read.csv("MasterSheets_Oct21_git/CET_MasterSheet_Dec21.csv")
str(d_CET)
## 'data.frame':    36 obs. of  16 variables:
##  $ Supervisor.PI                 : chr  "Wegener" "Wegener" "Wegener" "Wegener" ...
##  $ Diet                          : chr  "NS" "NS" "NS" "NS" ...
##  $ total...of.flies              : int  1897 793 1938 1355 2349 1418 1178 1544 2150 1236 ...
##  $ Batch                         : int  2 2 2 2 3 2 1 2 3 2 ...
##  $ Population                    : chr  "AK" "GI" "KA" "MA" ...
##  $ Line                          : chr  "mixed population" "mixed population" "mixed population" "mixed population" ...
##  $ Sex                           : chr  "mixed sex" "mixed sex" "mixed sex" "mixed sex" ...
##  $ ReplicateVial                 : logi  NA NA NA NA NA NA ...
##  $ Individual                    : logi  NA NA NA NA NA NA ...
##  $ Condition                     : chr  "18_LD_DD" "18_LD_DD" "18_LD_DD" "18_LD_DD" ...
##  $ CET_hours_MESA                : num  13.2 10.5 14 16.8 10.5 ...
##  $ CET_hours_LSPR                : num  12.5 10.5 11.7 17.5 11 ...
##  $ Period_MESA                   : num  24.2 24.8 24.1 23.3 24.9 ...
##  $ Period_LSPR                   : num  24.4 24.8 24.8 23.1 24.7 ...
##  $ Rhythmicity_LSPR_amplitude    : num  11.66 6.55 9.75 7.47 16.88 ...
##  $ Rhythmicity_JTK_p_BH_corrected: num  5.26e-24 6.27e-22 6.76e-14 2.92e-09 4.21e-24 ...
d_CET$Supervisor.PI <- as.factor(d_CET$Supervisor.PI)
d_CET$Diet <- as.factor(d_CET$Diet)
d_CET$Batch <- as.factor(d_CET$Batch)
d_CET$Population_Lat <- factor(d_CET$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_CET$Population_Lon <- factor(d_CET$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_CET$Population_Alt <- factor(d_CET$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_CET$Condition <- as.factor(d_CET$Condition)
d_CET$CET_hours_MESA <- as.numeric(d_CET$CET_hours_MESA)
d_CET$CET_hours_LSPR <- as.numeric(d_CET$CET_hours_LSPR)
d_CET$Period_MESA <- as.numeric(d_CET$Period_MESA)
d_CET$Period_LSPR <- as.numeric(d_CET$Period_LSPR)
d_CET$Rhythmicity_LSPR_amplitude <- as.numeric(d_CET$Rhythmicity_LSPR_amplitude)
d_CET$Rhythmicity_JTK_p_BH_corrected <- as.numeric(d_CET$Rhythmicity_JTK_p_BH_corrected)
str(d_CET)
## 'data.frame':    36 obs. of  19 variables:
##  $ Supervisor.PI                 : Factor w/ 1 level "Wegener": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet                          : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
##  $ total...of.flies              : int  1897 793 1938 1355 2349 1418 1178 1544 2150 1236 ...
##  $ Batch                         : Factor w/ 4 levels "1","2","3","4": 2 2 2 2 3 2 1 2 3 2 ...
##  $ Population                    : chr  "AK" "GI" "KA" "MA" ...
##  $ Line                          : chr  "mixed population" "mixed population" "mixed population" "mixed population" ...
##  $ Sex                           : chr  "mixed sex" "mixed sex" "mixed sex" "mixed sex" ...
##  $ ReplicateVial                 : logi  NA NA NA NA NA NA ...
##  $ Individual                    : logi  NA NA NA NA NA NA ...
##  $ Condition                     : Factor w/ 4 levels "18_LD_DD","18_LD_LD",..: 1 1 1 1 1 1 1 1 1 2 ...
##  $ CET_hours_MESA                : num  13.2 10.5 14 16.8 10.5 ...
##  $ CET_hours_LSPR                : num  12.5 10.5 11.7 17.5 11 ...
##  $ Period_MESA                   : num  24.2 24.8 24.1 23.3 24.9 ...
##  $ Period_LSPR                   : num  24.4 24.8 24.8 23.1 24.7 ...
##  $ Rhythmicity_LSPR_amplitude    : num  11.66 6.55 9.75 7.47 16.88 ...
##  $ Rhythmicity_JTK_p_BH_corrected: num  5.26e-24 6.27e-22 6.76e-14 2.92e-09 4.21e-24 ...
##  $ Population_Lat                : Factor w/ 9 levels "YE","RE","GI",..: 9 3 7 5 4 2 6 8 1 9 ...
##  $ Population_Lon                : Factor w/ 9 levels "RE","GI","KA",..: 6 2 3 5 4 1 7 9 8 6 ...
##  $ Population_Alt                : Factor w/ 9 levels "KA","AK","GI",..: 2 3 1 8 7 4 5 6 9 2 ...
d_CET_18_LD_DD <-subset(d_CET,Condition=='18_LD_DD')
d_CET_18_LD_LD <-subset(d_CET,Condition=='18_LD_LD')
d_CET_29_LD_DD <-subset(d_CET,Condition=='29_LD_DD')
d_CET_29_LD_LD <-subset(d_CET,Condition=='29_LD_LD')

Descriptive Statistics

18_LD_DD

Descriptive statistics for CET_hours_MESA :

Descriptive statistics for CET_hours_LSPR :

Descriptive statistics for Period_MESA :

Descriptive statistics for Period_LSPR :

Descriptive statistics for Rhythmicity_LSPR_amplitude :

Descriptive statistics for Rhythmicity_JTK_p_BH_corrected :

18_LD_LD

Descriptive statistics for CET_hours_MESA :

Descriptive statistics for CET_hours_LSPR :

Descriptive statistics for Period_MESA :

Descriptive statistics for Period_LSPR :

Descriptive statistics for Rhythmicity_LSPR_amplitude :

Descriptive statistics for Rhythmicity_JTK_p_BH_corrected :

29_LD_DD

Descriptive statistics for CET_hours_MESA :

Descriptive statistics for CET_hours_LSPR :

Descriptive statistics for Period_MESA :

Descriptive statistics for Period_LSPR :

Descriptive statistics for Rhythmicity_LSPR_amplitude :

Descriptive statistics for Rhythmicity_JTK_p_BH_corrected :

table_CET_29LDDD_Rhy_JTK <- write.csv(d_CET_29_LD_DD %>% group_by(Supervisor.PI, Condition, Population) %>% 
                                        summarise_at(vars(Rhythmicity_JTK_p_BH_corrected), 
                                                     list(Mean = mean)), 
                                      file = "CircadianEclosion/table_CET_29LDDD_Rhy_JTK.csv", row.names = T)
table_CET_29LDDD_Rhy_JTK <- read.csv("CircadianEclosion/table_CET_29LDDD_Rhy_JTK.csv")
DT::datatable(table_CET_29LDDD_Rhy_JTK, options = list(pageLength = 10)) %>% formatRound(5:5, 3)

29_LD_LD

Descriptive statistics for CET_hours_MESA :

Descriptive statistics for CET_hours_LSPR :

Descriptive statistics for Period_MESA :

Descriptive statistics for Period_LSPR :

Descriptive statistics for Rhythmicity_LSPR_amplitude :

Descriptive statistics for Rhythmicity_JTK_p_BH_corrected :

Plots and Linear Models by Lab

Overview

Wegener Lab

From Chris :

From Eran :

knitr::include_graphics("CircadianEclosion/FromEran/eclosion_data_Christian_Page_1.png")

knitr::include_graphics("CircadianEclosion/FromEran/eclosion_data_Christian_Page_2.png")

Locomotor activity

For a detailed description of tables, plots, linear models and outputs, please refer here 

#Note that the trait has been phenotyped only in males.

Contributors

Tauber Lab : Bettina Fishman, Eran Tauber

Data Reformatting

d_LA <- read.csv("MasterSheets_Oct21_git/LA_MasterSheet_Oct21.csv")
str(d_LA)
## 'data.frame':    639 obs. of  19 variables:
##  $ Supervisor.PI      : chr  "Tauber" "Tauber" "Tauber" "Tauber" ...
##  $ Diet               : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch              : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population         : chr  "YE" "YE" "YE" "YE" ...
##  $ Line               : chr  "YE27" "YE33" "YE40" "YE40" ...
##  $ Data.Label         : chr  "YE 29-9-18" "YE 33-9-18" "YE 40-9-18" "YE 40-9-18" ...
##  $ Sex                : chr  "M" "M" "M" "M" ...
##  $ ReplicateChamberOld: int  3 3 3 3 3 3 3 3 3 3 ...
##  $ ReplicateChamber   : chr  "Tauber_1_YE27_M_3" "Tauber_1_YE33_M_3" "Tauber_1_YE40_M_3" "Tauber_1_YE40_M_3" ...
##  $ Period             : num  23.6 20.2 24 24.2 23.1 ...
##  $ CircPhase          : num  23.21 13.07 4.59 9.32 12.98 ...
##  $ AbsPhase           : num  12.5 0.7 11.7 12.5 12.5 12 12.7 0.5 12.4 12.6 ...
##  $ Experiment         : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ND                 : num  0.678 0.95 1.083 0.948 1.455 ...
##  $ Activity           : num  29.2 46.8 45.4 37.8 37.8 31.8 43.6 42.8 29.6 21.6 ...
##  $ Country            : chr  "Turkey" "Turkey" "Turkey" "Turkey" ...
##  $ Latitude           : num  40.2 40.2 40.2 40.2 40.2 ...
##  $ Longitude          : num  32.3 32.3 32.3 32.3 32.3 ...
##  $ Altitude           : int  680 680 680 680 680 680 680 680 680 680 ...
d_LA$Supervisor.PI <- as.factor(d_LA$Supervisor.PI)
d_LA$Diet <- as.factor(d_LA$Diet)
d_LA$Batch <- as.factor(d_LA$Batch)
d_LA$Population_Lat <- factor(d_LA$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_LA$Population_Lon <- factor(d_LA$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_LA$Population_Alt <- factor(d_LA$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_LA$Line <- as.factor(d_LA$Line)
d_LA$ReplicateChamber <- as.factor(d_LA$ReplicateChamber)
d_LA$Period <- as.numeric(d_LA$Period)
d_LA$CircPhase <- as.numeric(d_LA$CircPhase)
d_LA$AbsPhase <- as.numeric(d_LA$AbsPhase)
d_LA$Experiment <- as.numeric(d_LA$Experiment)
d_LA$ND <- as.numeric(d_LA$ND)
d_LA$Activity <- as.numeric(d_LA$Activity)
str(d_LA)
## 'data.frame':    639 obs. of  22 variables:
##  $ Supervisor.PI      : Factor w/ 1 level "Tauber": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet               : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch              : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population         : chr  "YE" "YE" "YE" "YE" ...
##  $ Line               : Factor w/ 92 levels "AK1","AK2","AK3",..: 84 85 86 86 86 89 90 90 91 91 ...
##  $ Data.Label         : chr  "YE 29-9-18" "YE 33-9-18" "YE 40-9-18" "YE 40-9-18" ...
##  $ Sex                : chr  "M" "M" "M" "M" ...
##  $ ReplicateChamberOld: int  3 3 3 3 3 3 3 3 3 3 ...
##  $ ReplicateChamber   : Factor w/ 152 levels "Tauber_1_AK1_M_9",..: 76 77 78 78 78 79 80 80 81 81 ...
##  $ Period             : num  23.6 20.2 24 24.2 23.1 ...
##  $ CircPhase          : num  23.21 13.07 4.59 9.32 12.98 ...
##  $ AbsPhase           : num  12.5 0.7 11.7 12.5 12.5 12 12.7 0.5 12.4 12.6 ...
##  $ Experiment         : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ ND                 : num  0.678 0.95 1.083 0.948 1.455 ...
##  $ Activity           : num  29.2 46.8 45.4 37.8 37.8 31.8 43.6 42.8 29.6 21.6 ...
##  $ Country            : chr  "Turkey" "Turkey" "Turkey" "Turkey" ...
##  $ Latitude           : num  40.2 40.2 40.2 40.2 40.2 ...
##  $ Longitude          : num  32.3 32.3 32.3 32.3 32.3 ...
##  $ Altitude           : int  680 680 680 680 680 680 680 680 680 680 ...
##  $ Population_Lat     : Factor w/ 9 levels "YE","RE","GI",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population_Lon     : Factor w/ 9 levels "RE","GI","KA",..: 8 8 8 8 8 8 8 8 8 8 ...
##  $ Population_Alt     : Factor w/ 9 levels "KA","AK","GI",..: 9 9 9 9 9 9 9 9 9 9 ...

Descriptive Statistics

LA - Activity

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

LA - ND ratio

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

table_LA_ND_Pop_wobatch <- write.csv(d_LA %>% group_by(Supervisor.PI, Sex, Population) %>% 
                                        summarise_at(vars(ND), 
                                                     list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, 
                                                          SE = std_err, CV = coef_var, Mode = estimate_mode)), 
                                      file = "Locomotor/table_LA_ND_Pop_wobatch.csv", row.names = T)
table_LA_ND_Pop_wobatch <- read.csv("Locomotor/table_LA_ND_Pop_wobatch.csv")

LA - Circadian Phase

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Plots and Linear Models by Lab

Overview

Tauber Lab

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00214269 (tol = 0.002, component 1)
anova(LA_lmer_ND_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population 1.8666 0.23332     8 74.993  1.5771 0.1461
summary(LA_lmer_ND_Tauber)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ND ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: d_LA
## 
## REML criterion at convergence: 703.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1922 -0.4257 -0.0668  0.2481 11.4146 
## 
## Random effects:
##  Groups          Name        Variance  Std.Dev. 
##  Line:Population (Intercept) 3.972e-02 0.1993100
##  Batch           (Intercept) 1.608e-09 0.0000401
##  Residual                    1.479e-01 0.3846341
## Number of obs: 639, groups:  Line:Population, 92; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   0.86109    0.08409 63.09959  10.240 4.83e-15 ***
## PopulationGI -0.00947    0.12019 69.05962  -0.079   0.9374    
## PopulationKA  0.16250    0.11441 64.97528   1.420   0.1603    
## PopulationMA  0.09686    0.11425 65.05487   0.848   0.3997    
## PopulationMU  0.00468    0.12120 77.61572   0.039   0.9693    
## PopulationRE  0.01998    0.12567 74.00461   0.159   0.8741    
## PopulationUM -0.05699    0.11757 66.31573  -0.485   0.6295    
## PopulationVA  0.04927    0.11799 64.71625   0.418   0.6777    
## PopulationYE  0.22780    0.10520 73.45997   2.165   0.0336 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.700                                                 
## PopulatinKA -0.735  0.514                                          
## PopulatinMA -0.736  0.515  0.541                                   
## PopulatinMU -0.694  0.485  0.510  0.511                            
## PopulatinRE -0.669  0.468  0.492  0.493  0.464                     
## PopulatinUM -0.715  0.500  0.526  0.526  0.496  0.479              
## PopulatinVA -0.713  0.499  0.524  0.525  0.494  0.477  0.510       
## PopulatinYE -0.799  0.559  0.587  0.588  0.555  0.535  0.572  0.570
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00214269 (tol = 0.002, component 1)

CircPhase

anova(LA_lmer_CircPhase_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
## Population 1267.7  158.47     8 57.85  3.4025 0.002873 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LA_lmer_CircPhase_Tauber)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CircPhase ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: (d_LA)
## 
## REML criterion at convergence: 4247.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.10384 -0.57625 -0.06697  0.85374  1.83691 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  0.04096 0.2024  
##  Batch           (Intercept)  0.41358 0.6431  
##  Residual                    46.57281 6.8244  
## Number of obs: 639, groups:  Line:Population, 92; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)    9.0667     0.9362  8.2604   9.684 8.62e-06 ***
## PopulationGI   4.1447     1.1992 59.2979   3.456 0.001019 ** 
## PopulationKA   4.9461     1.1019 45.7989   4.489 4.81e-05 ***
## PopulationMA   3.8907     1.1176 52.8628   3.481 0.001009 ** 
## PopulationMU   3.1054     1.2721 79.9386   2.441 0.016851 *  
## PopulationRE   4.2240     1.3234 66.9784   3.192 0.002153 ** 
## PopulationUM   1.9322     1.1559 53.6304   1.672 0.100412    
## PopulationVA   3.9101     1.1388 47.0096   3.433 0.001254 ** 
## PopulationYE   3.8560     1.0930 73.7527   3.528 0.000725 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.596                                                 
## PopulatinKA -0.651  0.508                                          
## PopulatinMA -0.645  0.503  0.550                                   
## PopulatinMU -0.558  0.436  0.475  0.469                            
## PopulatinRE -0.559  0.435  0.479  0.481  0.400                     
## PopulatinUM -0.621  0.484  0.529  0.525  0.453  0.458              
## PopulatinVA -0.634  0.494  0.541  0.538  0.461  0.474  0.516       
## PopulatinYE -0.660  0.515  0.564  0.560  0.480  0.493  0.538  0.551

Activity

anova(LA_lmer_Activity_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 2149.2  268.65     8 71.722  3.8883 0.0007429 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LA_lmer_Activity_Tauber)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Activity ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: (d_LA)
## 
## REML criterion at convergence: 4624.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.16030 -0.55380  0.07697  0.65610  2.31656 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 41.72    6.459   
##  Batch           (Intercept)  7.27    2.696   
##  Residual                    69.09    8.312   
## Number of obs: 639, groups:  Line:Population, 92; Batch, 2
## 
## Fixed effects:
##              Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)    31.197      3.140  6.396   9.935 4.02e-05 ***
## PopulationGI    3.118      3.518 67.155   0.886   0.3786    
## PopulationKA    4.583      3.376 64.374   1.358   0.1793    
## PopulationMA    5.644      3.376 64.461   1.672   0.0994 .  
## PopulationMU   -6.072      3.498 72.038  -1.736   0.0869 .  
## PopulationRE    7.024      3.688 72.820   1.905   0.0608 .  
## PopulationUM   -5.237      3.465 64.916  -1.512   0.1355    
## PopulationVA   -2.690      3.486 65.345  -0.772   0.4431    
## PopulationYE    3.049      3.068 69.746   0.994   0.3237    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.563                                                 
## PopulatinKA -0.587  0.524                                          
## PopulatinMA -0.588  0.525  0.547                                   
## PopulatinMU -0.566  0.505  0.526  0.527                            
## PopulatinRE -0.543  0.484  0.505  0.508  0.484                     
## PopulatinUM -0.573  0.511  0.533  0.534  0.513  0.494              
## PopulatinVA -0.570  0.509  0.530  0.532  0.510  0.494  0.518       
## PopulatinYE -0.648  0.578  0.603  0.604  0.580  0.561  0.588  0.586

From Eran :

Red letters denote significant difference by Multiple Comparisons of Means: Tukey Contrasts

knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_1.png")

knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_2.png")

knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_3.png")

Phase in DD represented in angle. The mean and 95 confidence limits are shown. CI limits in red indicate uniform distribution (low concentration. i.e. Rayleigh test not significant).

knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_4.png")

Oriana 3 (Stacked circular).

knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_5.png")

Starvation resistance

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Gonzalez Lab : Llewellyn Green, Josefa Gonzalez, Miriam Merenciano

Onder Lab : Seda Coskun, Ekin Demir, Senel Selin Senkal, Cansu Aksoy

Pasyukova Lab : Alexander Symonenko, Natalia Roshina, Mikhail Trostnokov, Ekaterina Veselkina, Evgenia Tsybul’ko, Olga Rybina, Elena Pasyukova

Data Reformatting

d_SR <- read.csv("MasterSheets_Oct21_git/SR_MasterSheet_Nov21.csv")
str(d_SR)
## 'data.frame':    78315 obs. of  14 variables:
##  $ Supervisor.PI   : chr  "Gonzalez" "Gonzalez" "Gonzalez" "Gonzalez" ...
##  $ Diet            : chr  "S" "S" "S" "S" ...
##  $ Batch           : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Sex             : chr  "F" "F" "F" "F" ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : chr  "Gonzalez_1_AK1_F_1" "Gonzalez_1_AK1_F_1" "Gonzalez_1_AK1_F_1" "Gonzalez_1_AK1_F_1" ...
##  $ Individual      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ AgeAtDeath_hours: int  56 96 96 96 104 104 104 112 112 120 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
d_SR$Supervisor.PI <- as.factor(d_SR$Supervisor.PI)
d_SR$Diet <- as.factor(d_SR$Diet)
d_SR$Batch <- as.factor(d_SR$Batch)
d_SR$Population_Lat <- factor(d_SR$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_SR$Population_Lon <- factor(d_SR$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_SR$Population_Alt <- factor(d_SR$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_SR$Line <- as.factor(d_SR$Line)
d_SR$Sex <- as.factor(d_SR$Sex)
d_SR$ReplicateVial <- as.factor(d_SR$ReplicateVial)
d_SR$AgeAtDeath_hours <- as.numeric(d_SR$AgeAtDeath_hours)
str(d_SR)
## 'data.frame':    78315 obs. of  17 variables:
##  $ Supervisor.PI   : Factor w/ 3 levels "Gonzalez","Onder",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet            : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Batch           : Factor w/ 7 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population      : chr  "AK" "AK" "AK" "AK" ...
##  $ Line            : Factor w/ 171 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex             : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVialOld: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ReplicateVial   : Factor w/ 7753 levels "Gonzalez_1_AK1_F_1",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Individual      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ AgeAtDeath_hours: num  56 96 96 96 104 104 104 112 112 120 ...
##  $ Country         : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude        : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude       : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude        : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat  : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon  : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt  : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_SR_F <-subset(d_SR,Sex=='F')
d_SR_M <-subset(d_SR,Sex=='M')

Descriptive Statistics

Descriptive statistics at the line level, with batch information :

table_SR_Line_wbatch <- write.csv(d_SR %>% group_by(Supervisor.PI, Batch, Sex,Population, Line) %>% 
                                   summarise_at(vars(AgeAtDeath_hours), 
                                                list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, 
                                                     SE = std_err, CV = coef_var, Mode = estimate_mode)), 
                           file = "Starvation/table_SR_Line_wbatch.csv", row.names = T)
table_SR_Line_wbatch <- read.csv("Starvation/table_SR_Line_wbatch.csv")

Descriptive statistics at the line level, without batch information :

table_SR_Line_wobatch <- write.csv(d_SR %>% group_by(Supervisor.PI, Sex,Population, Line) %>% 
                                   summarise_at(vars(AgeAtDeath_hours), 
                                                list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, 
                                                     SE = std_err, CV = coef_var, Mode = estimate_mode)), 
                           file = "Starvation/table_SR_Line_wobatch.csv", row.names = T)
table_SR_Line_wobatch <- read.csv("Starvation/table_SR_Line_wobatch.csv")

Descriptive statistics at the population level, with batch information :

table_SR_Pop_wbatch <- write.csv(d_SR %>% group_by(Supervisor.PI, Batch, Sex, Population) %>% 
                                  summarise_at(vars(AgeAtDeath_hours), 
                                               list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, 
                                                    SE = std_err, CV = coef_var, Mode = estimate_mode)), 
                          file = "Starvation/table_SR_Pop_wbatch.csv", row.names = T)

table_SR_Pop_wbatch <- read.csv("Starvation/table_SR_Pop_wbatch.csv")

Descriptive statistics at the population level, without batch information :

table_SR_Pop_wobatch <- write.csv(d_SR %>% group_by(Supervisor.PI, Sex, Population) %>% 
                                  summarise_at(vars(AgeAtDeath_hours), 
                                               list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, 
                                                    SE = std_err, CV = coef_var, Mode = estimate_mode)), 
                          file = "Starvation/table_SR_Pop_wobatch.csv", row.names = T)

table_SR_Pop_wobatch <- read.csv("Starvation/table_SR_Pop_wobatch.csv")

Plots and Linear Models by Lab

Overview

min_SR <- min(d_SR$AgeAtDeath_hours)
max_SR <- max(d_SR$AgeAtDeath_hours)

Gonzalez Lab

anova(SR_F_lmer_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Population 9342.2  1167.8     8 134.99  3.4548 0.001186 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_F_lmer_Gonzalez)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |  
##     Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_SR_F, Supervisor.PI == "Gonzalez"))
## 
## REML criterion at convergence: 43270.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6657 -0.6316 -0.0456  0.5852  4.9362 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept) 117.17   10.824  
##  Line:Population               (Intercept) 126.21   11.235  
##  Batch                         (Intercept)  11.46    3.385  
##  Residual                                  338.01   18.385  
## Number of obs: 4900, groups:  
## ReplicateVial:Line:Population, 436; Line:Population, 141; Batch, 7
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)    85.162      3.289  71.153  25.897  < 2e-16 ***
## PopulationGI  -10.464      4.722 135.435  -2.216 0.028364 *  
## PopulationKA   -4.330      4.625 135.664  -0.936 0.350759    
## PopulationMA   -5.431      4.187 134.876  -1.297 0.196833    
## PopulationMU    3.440      4.239 134.819   0.812 0.418497    
## PopulationRE   -5.408      4.736 136.434  -1.142 0.255514    
## PopulationUM   -3.445      4.937 131.240  -0.698 0.486603    
## PopulationVA    3.236      4.421 132.504   0.732 0.465441    
## PopulationYE  -15.628      4.519 134.806  -3.458 0.000728 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.573                                                 
## PopulatinKA -0.591  0.404                                          
## PopulatinMA -0.643  0.444  0.454                                   
## PopulatinMU -0.621  0.435  0.442  0.491                            
## PopulatinRE -0.577  0.396  0.405  0.445  0.434                     
## PopulatinUM -0.540  0.375  0.383  0.423  0.417  0.375              
## PopulatinVA -0.594  0.416  0.424  0.469  0.465  0.414  0.398       
## PopulatinYE -0.578  0.406  0.413  0.459  0.455  0.404  0.390  0.436
anova(SR_M_lmer_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 6745.9  843.24     8 143.21  5.6436 3.145e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_M_lmer_Gonzalez)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |  
##     Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_SR_M, Supervisor.PI == "Gonzalez"))
## 
## REML criterion at convergence: 42786.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8769 -0.6117 -0.0757  0.5087  8.1754 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  49.61    7.043  
##  Line:Population               (Intercept)  60.30    7.766  
##  Batch                         (Intercept)  79.31    8.905  
##  Residual                                  149.42   12.224  
## Number of obs: 5335, groups:  
## ReplicateVial:Line:Population, 481; Line:Population, 156; Batch, 7
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   59.2899     3.9917  11.2314  14.853 9.84e-09 ***
## PopulationGI  -8.2127     3.1208 143.3562  -2.632 0.009430 ** 
## PopulationKA  -0.2066     3.1276 143.3321  -0.066 0.947426    
## PopulationMA  -5.3337     2.8943 143.1030  -1.843 0.067420 .  
## PopulationMU   5.5319     2.9260 143.1123   1.891 0.060697 .  
## PopulationRE  -4.4639     3.1794 146.2780  -1.404 0.162434    
## PopulationUM  -1.9244     3.2338 140.5597  -0.595 0.552726    
## PopulationVA  -1.4989     2.8848 141.4527  -0.520 0.604152    
## PopulationYE -11.5154     2.8977 143.1403  -3.974 0.000112 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.351                                                 
## PopulatinKA -0.356  0.443                                          
## PopulatinMA -0.378  0.477  0.478                                   
## PopulatinMU -0.362  0.467  0.464  0.503                            
## PopulatinRE -0.365  0.443  0.449  0.479  0.457                     
## PopulatinUM -0.332  0.424  0.423  0.458  0.452  0.420              
## PopulatinVA -0.378  0.477  0.478  0.515  0.504  0.477  0.458       
## PopulatinYE -0.381  0.477  0.479  0.515  0.502  0.481  0.457  0.515

Onder Lab

## boundary (singular) fit: see ?isSingular
anova(SR_F_lmer_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population  14650  1831.2     8 158.94  5.8393 1.537e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_F_lmer_Onder)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |  
##     Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_SR_F, Supervisor.PI == "Onder"))
## 
## REML criterion at convergence: 149434.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.0413 -0.6188 -0.0307  0.6086  3.9849 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)   9.722   3.118  
##  Line:Population               (Intercept) 168.283  12.972  
##  Batch                         (Intercept)   0.000   0.000  
##  Residual                                  313.603  17.709  
## Number of obs: 17282, groups:  
## ReplicateVial:Line:Population, 1737; Line:Population, 168; Batch, 5
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   92.6510     2.9360 158.9928  31.557  < 2e-16 ***
## PopulationGI  -4.4761     4.4849 159.0047  -0.998  0.31978    
## PopulationKA   4.5913     4.1521 158.9897   1.106  0.27049    
## PopulationMA  -0.2778     4.1522 159.0007  -0.067  0.94675    
## PopulationMU  10.1290     4.1512 158.8431   2.440  0.01579 *  
## PopulationRE  -3.8765     4.4033 158.8909  -0.880  0.37999    
## PopulationUM   0.4304     4.3319 159.0573   0.099  0.92098    
## PopulationVA   9.9510     4.1516 158.8990   2.397  0.01769 *  
## PopulationYE -12.5121     4.1522 158.9921  -3.013  0.00301 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655                                                 
## PopulatinKA -0.707  0.463                                          
## PopulatinMA -0.707  0.463  0.500                                   
## PopulatinMU -0.707  0.463  0.500  0.500                            
## PopulatinRE -0.667  0.436  0.471  0.471  0.472                     
## PopulatinUM -0.678  0.444  0.479  0.479  0.479  0.452              
## PopulatinVA -0.707  0.463  0.500  0.500  0.500  0.472  0.479       
## PopulatinYE -0.707  0.463  0.500  0.500  0.500  0.471  0.479  0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
#could simplufy the model, as some random factors explain very little
anova(SR_M_lmer_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 6159.6  769.95     8 158.28   4.128 0.0001689 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_M_lmer_Onder)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |  
##     Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_SR_M, Supervisor.PI == "Onder"))
## 
## REML criterion at convergence: 139728.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1862 -0.6258 -0.0367  0.5970  5.2151 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  10.5531  3.2486 
##  Line:Population               (Intercept)  95.9372  9.7948 
##  Batch                         (Intercept)   0.8989  0.9481 
##  Residual                                  186.5180 13.6572 
## Number of obs: 17158, groups:  
## ReplicateVial:Line:Population, 1732; Line:Population, 168; Batch, 5
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   73.0727     2.2707 131.3319  32.181   <2e-16 ***
## PopulationGI  -3.4277     3.4025 158.4759  -1.007   0.3153    
## PopulationKA   2.3717     3.1545 159.2335   0.752   0.4533    
## PopulationMA   0.1421     3.1452 157.4699   0.045   0.9640    
## PopulationMU   6.7401     3.1446 157.3228   2.143   0.0336 *  
## PopulationRE  -3.0209     3.3370 157.6650  -0.905   0.3667    
## PopulationUM   3.2483     3.2930 159.5561   0.986   0.3254    
## PopulationVA   4.6345     3.1456 157.5723   1.473   0.1427    
## PopulationYE  -8.1979     3.1460 157.6663  -2.606   0.0100 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640                                                 
## PopulatinKA -0.696  0.463                                          
## PopulatinMA -0.694  0.462  0.499                                   
## PopulatinMU -0.693  0.461  0.498  0.500                            
## PopulatinRE -0.653  0.435  0.470  0.471  0.471                     
## PopulatinUM -0.667  0.443  0.483  0.479  0.477  0.450              
## PopulatinVA -0.695  0.462  0.500  0.500  0.500  0.471  0.480       
## PopulatinYE -0.694  0.462  0.501  0.500  0.500  0.471  0.480  0.500

Pasyukova Lab

anova(SR_F_lmer_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population  17772  2221.5     8 159.75  5.9619 1.092e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_F_lmer_Pasyukova)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |  
##     Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_SR_F, Supervisor.PI == "Pasyukova"))
## 
## REML criterion at convergence: 149510.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.1750 -0.5760  0.0268  0.6125  3.5353 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept) 101.25   10.062  
##  Line:Population               (Intercept) 113.34   10.646  
##  Batch                         (Intercept)  73.72    8.586  
##  Residual                                  372.62   19.303  
## Number of obs: 16779, groups:  
## ReplicateVial:Line:Population, 1681; Line:Population, 169; Batch, 5
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   103.162      4.626   5.314  22.301 1.86e-06 ***
## PopulationGI    5.103      3.947 159.643   1.293   0.1979    
## PopulationKA    1.952      3.486 159.684   0.560   0.5764    
## PopulationMA   -1.843      3.485 159.601  -0.529   0.5976    
## PopulationMU    2.658      3.485 159.608   0.762   0.4469    
## PopulationRE    0.366      3.706 159.554   0.099   0.9214    
## PopulationUM    0.779      3.587 159.855   0.217   0.8283    
## PopulationVA    7.520      3.486 159.633   2.158   0.0325 *  
## PopulationYE  -14.686      3.488 160.062  -4.211 4.24e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.321                                                 
## PopulatinKA -0.361  0.421                                          
## PopulatinMA -0.359  0.421  0.476                                   
## PopulatinMU -0.359  0.421  0.476  0.476                            
## PopulatinRE -0.338  0.396  0.448  0.448  0.448                     
## PopulatinUM -0.349  0.409  0.463  0.463  0.463  0.435              
## PopulatinVA -0.359  0.421  0.476  0.476  0.476  0.448  0.463       
## PopulatinYE -0.361  0.421  0.476  0.476  0.476  0.447  0.463  0.476
anova(SR_M_lmer_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population  13147  1643.3     8 159.66  7.1587 4.403e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_M_lmer_Pasyukova)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |  
##     Batch) + (1 | ReplicateVial:Line:Population)
##    Data: (subset(d_SR_M, Supervisor.PI == "Pasyukova"))
## 
## REML criterion at convergence: 141797.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5586 -0.6043 -0.0033  0.5733  5.7707 
## 
## Random effects:
##  Groups                        Name        Variance Std.Dev.
##  ReplicateVial:Line:Population (Intercept)  49.00    7.000  
##  Line:Population               (Intercept)  63.87    7.992  
##  Batch                         (Intercept)  11.53    3.395  
##  Residual                                  229.56   15.151  
## Number of obs: 16861, groups:  
## ReplicateVial:Line:Population, 1687; Line:Population, 169; Batch, 5
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   68.41419    2.39673  20.26738  28.545  < 2e-16 ***
## PopulationGI   0.05819    2.94895 159.38318   0.020 0.984281    
## PopulationKA   4.01793    2.60474 159.45628   1.543 0.124924    
## PopulationMA   0.91437    2.60464 159.43197   0.351 0.726012    
## PopulationMU   6.73374    2.60442 159.37742   2.586 0.010619 *  
## PopulationRE   0.61952    2.77094 159.65533   0.224 0.823372    
## PopulationUM   3.52710    2.67959 159.48532   1.316 0.189967    
## PopulationVA   6.95578    2.60518 159.56251   2.670 0.008371 ** 
## PopulationYE  -9.98686    2.60612 159.78609  -3.832 0.000182 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.457                                                 
## PopulatinKA -0.519  0.420                                          
## PopulatinMA -0.518  0.420  0.476                                   
## PopulatinMU -0.517  0.420  0.476  0.476                            
## PopulatinRE -0.492  0.395  0.448  0.447  0.447                     
## PopulatinUM -0.503  0.409  0.463  0.463  0.463  0.435              
## PopulatinVA -0.517  0.420  0.476  0.476  0.476  0.447  0.463       
## PopulatinYE -0.523  0.420  0.476  0.475  0.476  0.448  0.462  0.475

Survival Analyses

Gonzalez Lab

Onder Lab

Pasyukova Lab

coxme

#Please refer to "_SurvivalAnalyses_" folder
# adding a Censor column, with all individuals being kept
d_SR_surv <- d_SR %>% mutate(Censor = 1)

Lab correlations

There are 0 populations and 156 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Pigmentation

For a detailed description of tables, plots, linear models and outputs, please refer here

Contributors

Abbott Lab : Jessica Abbott, Qinyang Li, Shahzad Khan

Gibert Lab :Cristina Vieira, Laurence Mouton, Natacha Kremer, Sonia Martinez, Camille Mermet, Patricia Gibert

Schmidt Lab : Amy Goldfischer, Paul Schmidt

Data Reformatting

d_Pgm <- read.csv("MasterSheets_Oct21_git/PGM_MasterSheet_Jan22.csv")
str(d_Pgm)
## 'data.frame':    3694 obs. of  21 variables:
##  $ Supervisor.PI: chr  "Gibert" "Gibert" "Gibert" "Gibert" ...
##  $ Diet         : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch        : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population   : chr  "AK" "AK" "AK" "AK" ...
##  $ Line         : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Sex          : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ Individual   : int  1 2 3 4 5 6 7 8 9 1 ...
##  $ AreaT4       : int  12439 10230 7525 14898 9160 8279 13100 9016 7776 9595 ...
##  $ AreaT5       : int  10410 8105 7358 7682 8931 8518 16468 10331 7527 8454 ...
##  $ AreaT6       : int  8667 6063 6732 5718 8630 10349 11798 7986 5248 8749 ...
##  $ PercT4       : num  13.6 28.1 41.4 25.4 17.3 ...
##  $ PercT5       : num  27.8 45.8 62.7 37.4 32.8 ...
##  $ PercT6       : num  53.9 71 78 44.8 40 ...
##  $ TotalArea    : int  31516 24398 21615 28298 26721 27146 41366 27333 20551 26798 ...
##  $ TotalBlack   : num  9270 10900 12986 9220 7966 ...
##  $ TotalPerc    : num  29.4 44.7 60.1 32.6 29.8 ...
##  $ Orientation  : chr  "OK" "OK" "OK" "OK" ...
##  $ Country      : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude     : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude    : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude     : int  88 88 88 88 88 88 88 88 88 88 ...
d_Pgm$Supervisor.PI <- as.factor(d_Pgm$Supervisor.PI)
d_Pgm$Diet <- as.factor(d_Pgm$Diet)
d_Pgm$Batch <- as.factor(d_Pgm$Batch)
d_Pgm$Population_Lat <- factor(d_Pgm$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Pgm$Population_Lon <- factor(d_Pgm$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Pgm$Population_Alt <- factor(d_Pgm$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Pgm$Line <- as.factor(d_Pgm$Line)
d_Pgm$AreaT4 <- as.numeric(d_Pgm$AreaT4)
d_Pgm$AreaT5 <- as.numeric(d_Pgm$AreaT5)
d_Pgm$AreaT6 <- as.numeric(d_Pgm$AreaT6)
d_Pgm$PercT4 <- as.numeric(d_Pgm$PercT4)
d_Pgm$PercT5 <- as.numeric(d_Pgm$PercT5)
d_Pgm$PercT6 <- as.numeric(d_Pgm$PercT6)
d_Pgm$TotalArea <- as.numeric(d_Pgm$TotalArea)
d_Pgm$TotalBlack <- as.numeric(d_Pgm$TotalBlack)
d_Pgm$Latitude <- as.numeric(d_Pgm$Latitude)
d_Pgm$Longitude <- as.numeric(d_Pgm$Latitude)
d_Pgm$Altitude <- as.numeric(d_Pgm$Altitude)
str(d_Pgm)
## 'data.frame':    3694 obs. of  24 variables:
##  $ Supervisor.PI : Factor w/ 2 levels "Abbott","Gibert": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Diet          : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch         : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population    : chr  "AK" "AK" "AK" "AK" ...
##  $ Line          : Factor w/ 168 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 11 ...
##  $ Sex           : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ Individual    : int  1 2 3 4 5 6 7 8 9 1 ...
##  $ AreaT4        : num  12439 10230 7525 14898 9160 ...
##  $ AreaT5        : num  10410 8105 7358 7682 8931 ...
##  $ AreaT6        : num  8667 6063 6732 5718 8630 ...
##  $ PercT4        : num  13.6 28.1 41.4 25.4 17.3 ...
##  $ PercT5        : num  27.8 45.8 62.7 37.4 32.8 ...
##  $ PercT6        : num  53.9 71 78 44.8 40 ...
##  $ TotalArea     : num  31516 24398 21615 28298 26721 ...
##  $ TotalBlack    : num  9270 10900 12986 9220 7966 ...
##  $ TotalPerc     : num  29.4 44.7 60.1 32.6 29.8 ...
##  $ Orientation   : chr  "OK" "OK" "OK" "OK" ...
##  $ Country       : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude      : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude     : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Altitude      : num  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat: Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon: Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt: Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_Pgm2 <- read.csv("MasterSheets_Oct21_git/PGM2_MasterSheet_Oct21.csv")
str(d_Pgm2)
## 'data.frame':    1514 obs. of  15 variables:
##  $ Supervisor.PI: chr  "Schmidt" "Schmidt" "Schmidt" "Schmidt" ...
##  $ Diet         : chr  "NS" "NS" "NS" "NS" ...
##  $ Batch        : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Population   : chr  "AK" "AK" "AK" "AK" ...
##  $ Line         : chr  "AK1" "AK1" "AK1" "AK1" ...
##  $ Sex          : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ Individual   : int  2 9 1 10 3 6 8 5 4 7 ...
##  $ Tergite8     : int  4 3 2 2 2 2 2 1 1 1 ...
##  $ Tergite9     : int  8 7 6 5 4 4 4 3 2 1 ...
##  $ Tergite10    : int  6 3 3 4 1 1 1 1 0 0 ...
##  $ Total        : int  18 13 11 11 7 7 7 5 3 2 ...
##  $ Country      : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude     : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude    : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude     : int  88 88 88 88 88 88 88 88 88 88 ...
d_Pgm2$Supervisor.PI <- as.factor(d_Pgm2$Supervisor.PI)
d_Pgm2$Diet <- as.factor(d_Pgm2$Diet)
d_Pgm2$Batch <- as.factor(d_Pgm2$Batch)
d_Pgm2$Population_Lat <- factor(d_Pgm2$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Pgm2$Population_Lon <- factor(d_Pgm2$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Pgm2$Population_Alt <- factor(d_Pgm2$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Pgm2$Line <- as.factor(d_Pgm2$Line)
d_Pgm2$Tergite8 <- as.numeric(d_Pgm2$Tergite8)
d_Pgm2$Tergite9 <- as.numeric(d_Pgm2$Tergite9)
d_Pgm2$Tergite10 <- as.numeric(d_Pgm2$Tergite10)
d_Pgm2$Total <- as.numeric(d_Pgm2$Total)
str(d_Pgm2)
## 'data.frame':    1514 obs. of  18 variables:
##  $ Supervisor.PI : Factor w/ 1 level "Schmidt": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diet          : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Batch         : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population    : chr  "AK" "AK" "AK" "AK" ...
##  $ Line          : Factor w/ 157 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex           : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ Individual    : int  2 9 1 10 3 6 8 5 4 7 ...
##  $ Tergite8      : num  4 3 2 2 2 2 2 1 1 1 ...
##  $ Tergite9      : num  8 7 6 5 4 4 4 3 2 1 ...
##  $ Tergite10     : num  6 3 3 4 1 1 1 1 0 0 ...
##  $ Total         : num  18 13 11 11 7 7 7 5 3 2 ...
##  $ Country       : chr  "Finland" "Finland" "Finland" "Finland" ...
##  $ Latitude      : num  61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
##  $ Longitude     : num  23.5 23.5 23.5 23.5 23.5 ...
##  $ Altitude      : int  88 88 88 88 88 88 88 88 88 88 ...
##  $ Population_Lat: Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
##  $ Population_Lon: Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Population_Alt: Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...

Descriptive Statistics

T4

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

T5

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

T6

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

DT::datatable(table_Pgm_Line_PercT6_wobatch, options = list(pageLength = 10)) %>% formatRound(5:12, 3)

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Total

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

T8

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

T9

Descriptive statistics at the line level, with batch information :

DT::datatable(table_Pgm_Line_Tergite9_wbatch, options = list(pageLength = 10)) %>% formatRound(6:13, 3)

Descriptive statistics at the line level, without batch information :

table_Pgm_Line_Tergite9_wobatch <- write.csv(d_Pgm2 %>% group_by(Supervisor.PI, Population, Line) %>% summarise_at(vars(Tergite9), list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, SE = std_err, CV = coef_var, Mode = estimate_mode)), file = "Pigmentation/table_Pgm_Line_Tergite9_wobatch.csv", row.names = T)
table_Pgm_Line_Tergite9_wobatch <- read.csv("Pigmentation/table_Pgm_Line_Tergite9_wobatch.csv")

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

T10

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Total2

Descriptive statistics at the line level, with batch information :

Descriptive statistics at the line level, without batch information :

Descriptive statistics at the population level, with batch information :

Descriptive statistics at the population level, without batch information :

Overview

Plots and Linear Models by Lab

Abbott Lab

PercT4

anova(Pgm_lmer_Abbott_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 3877.1  484.64     8 151.94  15.681 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Abbott_PercT4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT4 ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm, Supervisor.PI == "Abbott"))
## 
## REML criterion at convergence: 12537.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1997 -0.5926 -0.1274  0.4734  7.9312 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 16.14    4.018   
##  Residual                    30.91    5.559   
## Number of obs: 1953, groups:  Line:Population, 161
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   14.2768     0.9638 149.7585  14.813  < 2e-16 ***
## PopulationGI  -1.0753     1.5064 151.5438  -0.714  0.47646    
## PopulationKA  -4.1250     1.3840 151.1631  -2.980  0.00335 ** 
## PopulationMA   7.3325     1.4042 151.3960   5.222 5.76e-07 ***
## PopulationMU   0.9357     1.4241 150.7292   0.657  0.51216    
## PopulationRE   3.0695     1.4497 151.4246   2.117  0.03586 *  
## PopulationUM   0.4006     1.4028 150.8162   0.286  0.77560    
## PopulationVA  -6.7387     1.3818 150.1755  -4.877 2.72e-06 ***
## PopulationYE   0.6671     1.3665 151.3040   0.488  0.62612    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640                                                 
## PopulatinKA -0.696  0.446                                          
## PopulatinMA -0.686  0.439  0.478                                   
## PopulatinMU -0.677  0.433  0.471  0.465                            
## PopulatinRE -0.665  0.425  0.463  0.456  0.450                     
## PopulatinUM -0.687  0.440  0.478  0.472  0.465  0.457              
## PopulatinVA -0.698  0.446  0.486  0.479  0.472  0.464  0.479       
## PopulatinYE -0.705  0.451  0.491  0.484  0.477  0.469  0.485  0.492

PercT5

anova(Pgm_lmer_Abbott_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)    
## Population 3860.7  482.59     8 151.25  13.947 4.48e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Abbott_PercT5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT5 ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm, Supervisor.PI == "Abbott"))
## 
## REML criterion at convergence: 12736.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1419 -0.6487 -0.1115  0.5154  6.4092 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 15.44    3.929   
##  Residual                    34.60    5.882   
## Number of obs: 1953, groups:  Line:Population, 161
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   17.74944    0.95309 148.74070  18.623  < 2e-16 ***
## PopulationGI  -1.92457    1.49026 150.78266  -1.291 0.198531    
## PopulationKA  -5.13874    1.36905 150.35418  -3.754 0.000249 ***
## PopulationMA   5.92107    1.38905 150.61490   4.263 3.55e-05 ***
## PopulationMU  -0.44385    1.40858 149.86021  -0.315 0.753123    
## PopulationRE   0.66321    1.43411 150.64551   0.462 0.644426    
## PopulationUM  -0.03034    1.38750 149.95903  -0.022 0.982586    
## PopulationVA  -7.31175    1.36649 149.22920  -5.351 3.23e-07 ***
## PopulationYE  -0.79873    1.35175 150.51384  -0.591 0.555483    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640                                                 
## PopulatinKA -0.696  0.445                                          
## PopulatinMA -0.686  0.439  0.478                                   
## PopulatinMU -0.677  0.433  0.471  0.464                            
## PopulatinRE -0.665  0.425  0.463  0.456  0.450                     
## PopulatinUM -0.687  0.439  0.478  0.471  0.465  0.457              
## PopulatinVA -0.697  0.446  0.486  0.479  0.472  0.464  0.479       
## PopulatinYE -0.705  0.451  0.491  0.484  0.477  0.469  0.484  0.492

PercT6

anova(Pgm_lmer_Abbott_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## Population 5124.6  640.58     8 151.7  4.0012 0.0002482 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Abbott_PercT6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT6 ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm, Supervisor.PI == "Abbott"))
## 
## REML criterion at convergence: 15794.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3191 -0.6368 -0.0337  0.5949  3.1370 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 130.3    11.42   
##  Residual                    160.1    12.65   
## Number of obs: 1953, groups:  Line:Population, 161
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   34.3826     2.6738 150.2519  12.859  < 2e-16 ***
## PopulationGI  -0.4482     4.1750 151.4446  -0.107 0.914645    
## PopulationKA  -1.5120     3.8365 151.1807  -0.394 0.694052    
## PopulationMA  13.1600     3.8919 151.3439   3.381 0.000918 ***
## PopulationMU  -2.9065     3.9487 150.8882  -0.736 0.462828    
## PopulationRE   8.1644     4.0180 151.3659   2.032 0.043909 *  
## PopulationUM   7.4534     3.8893 150.9472   1.916 0.057207 .  
## PopulationVA  -0.4916     3.8323 150.5155  -0.128 0.898091    
## PopulationYE  -1.9175     3.7876 151.2768  -0.506 0.613420    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640                                                 
## PopulatinKA -0.697  0.446                                          
## PopulatinMA -0.687  0.440  0.479                                   
## PopulatinMU -0.677  0.434  0.472  0.465                            
## PopulatinRE -0.665  0.426  0.464  0.457  0.451                     
## PopulatinUM -0.687  0.440  0.479  0.472  0.466  0.457              
## PopulatinVA -0.698  0.447  0.486  0.479  0.472  0.464  0.480       
## PopulatinYE -0.706  0.452  0.492  0.485  0.478  0.470  0.485  0.493

TotalPerc

anova(Pgm_lmer_Abbott_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 3253.6   406.7     8 151.49  11.886 4.508e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Abbott_TotalPerc)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TotalPerc ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm, Supervisor.PI == "Abbott"))
## 
## REML criterion at convergence: 12772.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7535 -0.6401 -0.0873  0.5628  4.8794 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 23.64    4.862   
##  Residual                    34.22    5.849   
## Number of obs: 1953, groups:  Line:Population, 161
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   21.0192     1.1475 149.8020  18.317  < 2e-16 ***
## PopulationGI  -1.2789     1.7924 151.1867  -0.714  0.47663    
## PopulationKA  -3.4048     1.6470 150.8841  -2.067  0.04041 *  
## PopulationMA   9.0080     1.6708 151.0706   5.391 2.64e-07 ***
## PopulationMU  -0.4718     1.6950 150.5454  -0.278  0.78113    
## PopulationRE   4.1260     1.7250 151.0950   2.392  0.01799 *  
## PopulationUM   2.7754     1.6695 150.6136   1.662  0.09851 .  
## PopulationVA  -4.8768     1.6449 150.1137  -2.965  0.00352 ** 
## PopulationYE  -0.2038     1.6260 150.9949  -0.125  0.90043    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640                                                 
## PopulatinKA -0.697  0.446                                          
## PopulatinMA -0.687  0.440  0.479                                   
## PopulatinMU -0.677  0.433  0.472  0.465                            
## PopulatinRE -0.665  0.426  0.464  0.457  0.450                     
## PopulatinUM -0.687  0.440  0.479  0.472  0.465  0.457              
## PopulatinVA -0.698  0.447  0.486  0.479  0.472  0.464  0.480       
## PopulatinYE -0.706  0.452  0.492  0.485  0.478  0.469  0.485  0.492

Gibert Lab

PercT4

anova(Pgm_lmer_Gibert_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 1861.6   232.7     8 151.19  2.5618 0.01198 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Gibert_PercT4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT4 ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: (subset(d_Pgm, Supervisor.PI == "Gibert"))
## 
## REML criterion at convergence: 12935.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4920 -0.6719 -0.1283  0.5731  5.9803 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 15.601   3.950   
##  Batch           (Intercept)  3.226   1.796   
##  Residual                    90.836   9.531   
## Number of obs: 1741, groups:  Line:Population, 167; Batch, 3
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   24.28251    1.57941   4.55352  15.374 4.25e-05 ***
## PopulationGI  -0.88788    1.69837 151.69405  -0.523   0.6019    
## PopulationKA  -3.42364    1.57466 152.48388  -2.174   0.0312 *  
## PopulationMA  -0.50222    1.59924 153.61400  -0.314   0.7539    
## PopulationMU   0.78961    1.57540 152.75242   0.501   0.6169    
## PopulationRE  -4.31393    1.66776 151.86314  -2.587   0.0106 *  
## PopulationUM   0.19090    1.61666 152.21307   0.118   0.9062    
## PopulationVA   0.02432    1.59518 152.39674   0.015   0.9879    
## PopulationYE   1.11868    1.58777 156.43329   0.705   0.4821    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.425                                                 
## PopulatinKA -0.459  0.469                                          
## PopulatinMA -0.452  0.462  0.500                                   
## PopulatinMU -0.458  0.469  0.508  0.500                            
## PopulatinRE -0.432  0.445  0.478  0.471  0.478                     
## PopulatinUM -0.447  0.457  0.495  0.487  0.494  0.466              
## PopulatinVA -0.453  0.463  0.501  0.494  0.501  0.472  0.488       
## PopulatinYE -0.455  0.466  0.504  0.496  0.503  0.474  0.491  0.497

PercT5

anova(Pgm_lmer_Gibert_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 1803.1  225.39     8 157.32  2.1354 0.03548 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Gibert_PercT5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT5 ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm, Supervisor.PI == "Gibert"))
## 
## REML criterion at convergence: 13270.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9328 -0.6447 -0.0768  0.5422  5.7227 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept)  36.17    6.014  
##  Residual                    105.55   10.274  
## Number of obs: 1741, groups:  Line:Population, 167
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   32.60294    1.52456 156.95329  21.385   <2e-16 ***
## PopulationGI  -3.45120    2.32659 156.32514  -1.483   0.1400    
## PopulationKA  -3.27749    2.15540 156.76946  -1.521   0.1304    
## PopulationMA   3.79005    2.18722 157.65924   1.733   0.0851 .  
## PopulationMU   1.93616    2.15605 156.95329   0.898   0.3706    
## PopulationRE  -1.70093    2.28506 156.44381  -0.744   0.4578    
## PopulationUM   0.08672    2.21401 156.63100   0.039   0.9688    
## PopulationVA  -0.54513    2.18337 156.70526  -0.250   0.8032    
## PopulationYE   0.60097    2.16758 159.78466   0.277   0.7819    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655                                                 
## PopulatinKA -0.707  0.463                                          
## PopulatinMA -0.697  0.457  0.493                                   
## PopulatinMU -0.707  0.463  0.500  0.493                            
## PopulatinRE -0.667  0.437  0.472  0.465  0.472                     
## PopulatinUM -0.689  0.451  0.487  0.480  0.487  0.459              
## PopulatinVA -0.698  0.458  0.494  0.487  0.494  0.466  0.481       
## PopulatinYE -0.703  0.461  0.497  0.490  0.497  0.469  0.484  0.491

PercT6

anova(Pgm_lmer_Gibert_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population 2429.4  303.67     8 158.47  1.4648 0.1741
summary(Pgm_lmer_Gibert_PercT6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT6 ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: (subset(d_Pgm, Supervisor.PI == "Gibert"))
## 
## REML criterion at convergence: 14560.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0930 -0.6255  0.0378  0.6407  2.8805 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 173.498  13.172  
##  Batch           (Intercept)   9.633   3.104  
##  Residual                    207.318  14.399  
## Number of obs: 1741, groups:  Line:Population, 167; Batch, 3
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)   46.7258     3.6874  10.3399  12.672 1.25e-07 ***
## PopulationGI  -0.5588     4.7688 158.8324  -0.117   0.9069    
## PopulationKA  11.3249     4.4166 158.9499   2.564   0.0113 *  
## PopulationMA   8.3252     4.4785 159.4865   1.859   0.0649 .  
## PopulationMU   5.6397     4.4172 159.0392   1.277   0.2035    
## PopulationRE   7.5091     4.6823 158.7993   1.604   0.1108    
## PopulationUM   6.9007     4.5365 158.8765   1.521   0.1302    
## PopulationVA   4.6139     4.4746 158.9709   1.031   0.3040    
## PopulationYE   2.3744     4.4294 160.6488   0.536   0.5927    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.534                                                 
## PopulatinKA -0.577  0.466                                          
## PopulatinMA -0.569  0.459  0.497                                   
## PopulatinMU -0.577  0.465  0.503  0.497                            
## PopulatinRE -0.544  0.440  0.474  0.468  0.474                     
## PopulatinUM -0.562  0.453  0.490  0.484  0.490  0.462              
## PopulatinVA -0.570  0.459  0.497  0.490  0.497  0.468  0.484       
## PopulatinYE -0.575  0.464  0.502  0.495  0.502  0.473  0.489  0.496

TotalPerc

## boundary (singular) fit: see ?isSingular
anova(Pgm_lmer_Gibert_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Population 497.78  62.223     8 157.94  0.8462 0.5634
summary(Pgm_lmer_Gibert_TotalPerc)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TotalPerc ~ Population + (1 | Line:Population) + (1 | Batch)
##    Data: (subset(d_Pgm, Supervisor.PI == "Gibert"))
## 
## REML criterion at convergence: 12682.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2632 -0.6733 -0.0496  0.6084  4.5241 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 34.06    5.836   
##  Batch           (Intercept)  0.00    0.000   
##  Residual                    73.53    8.575   
## Number of obs: 1741, groups:  Line:Population, 167; Batch, 3
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   33.30713    1.43614 157.60162  23.192   <2e-16 ***
## PopulationGI  -1.88256    2.19213 157.11869  -0.859    0.392    
## PopulationKA   0.13794    2.03052 157.45431   0.068    0.946    
## PopulationMA   2.82784    2.05986 158.20548   1.373    0.172    
## PopulationMU   2.07261    2.03101 157.60162   1.020    0.309    
## PopulationRE   0.04646    2.15291 157.20835   0.022    0.983    
## PopulationUM   1.99631    2.08584 157.34973   0.957    0.340    
## PopulationVA   1.23563    2.05692 157.40581   0.601    0.549    
## PopulationYE   1.19800    2.03985 159.99603   0.587    0.558    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655                                                 
## PopulatinKA -0.707  0.463                                          
## PopulatinMA -0.697  0.457  0.493                                   
## PopulatinMU -0.707  0.463  0.500  0.493                            
## PopulatinRE -0.667  0.437  0.472  0.465  0.472                     
## PopulatinUM -0.689  0.451  0.487  0.480  0.487  0.459              
## PopulatinVA -0.698  0.457  0.494  0.487  0.494  0.466  0.481       
## PopulatinYE -0.704  0.461  0.498  0.491  0.498  0.470  0.485  0.492
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

Schmidt Lab

Tergite8

anova(Pgm2_lmer_Schmidt_Tergite8)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## Population 22.509  2.8137     8 147.42  3.9144 0.0003221 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm2_lmer_Schmidt_Tergite8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Tergite8 ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm2, Supervisor.PI == "Schmidt"))
## 
## REML criterion at convergence: 4128.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3233 -0.6262 -0.0680  0.5288  7.2893 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 0.5292   0.7274  
##  Residual                    0.7188   0.8478  
## Number of obs: 1514, groups:  Line:Population, 157
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    2.27000    0.17336 145.49306  13.094   <2e-16 ***
## PopulationGI  -0.26045    0.27736 147.80273  -0.939   0.3492    
## PopulationKA   0.22474    0.24837 145.49306   0.905   0.3670    
## PopulationMA   0.58606    0.24893 146.62982   2.354   0.0199 *  
## PopulationMU   0.01145    0.24532 145.83263   0.047   0.9628    
## PopulationRE   0.20939    0.27165 148.53344   0.771   0.4421    
## PopulationUM   0.53207    0.26518 146.26029   2.006   0.0467 *  
## PopulationVA  -0.62944    0.25587 145.75515  -2.460   0.0151 *  
## PopulationYE   0.16110    0.24595 147.30477   0.655   0.5135    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.625                                                 
## PopulatinKA -0.698  0.436                                          
## PopulatinMA -0.696  0.435  0.486                                   
## PopulatinMU -0.707  0.442  0.493  0.492                            
## PopulatinRE -0.638  0.399  0.445  0.444  0.451                     
## PopulatinUM -0.654  0.409  0.456  0.455  0.462  0.417              
## PopulatinVA -0.678  0.423  0.473  0.472  0.479  0.432  0.443       
## PopulatinYE -0.705  0.441  0.492  0.491  0.498  0.450  0.461  0.478

Tergite9

anova(Pgm2_lmer_Schmidt_Tergite9)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 31.677  3.9597     8 148.1  1.5538 0.1436
summary(Pgm2_lmer_Schmidt_Tergite9)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Tergite9 ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm2, Supervisor.PI == "Schmidt"))
## 
## REML criterion at convergence: 6123.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2209 -0.6482 -0.0807  0.5799  3.6192 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 3.671    1.916   
##  Residual                    2.548    1.596   
## Number of obs: 1514, groups:  Line:Population, 157
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    4.5450     0.4430 146.9794  10.258   <2e-16 ***
## PopulationGI  -0.4001     0.7075 148.3076  -0.566   0.5726    
## PopulationKA   0.4866     0.6348 146.9794   0.767   0.4446    
## PopulationMA   0.9622     0.6356 147.6853   1.514   0.1322    
## PopulationMU   0.3373     0.6268 147.1736   0.538   0.5913    
## PopulationRE   0.9606     0.6926 148.7380   1.387   0.1675    
## PopulationUM   1.7584     0.6773 147.4275   2.596   0.0104 *  
## PopulationVA   0.3181     0.6538 147.1266   0.487   0.6273    
## PopulationYE   0.2175     0.6277 148.0006   0.347   0.7294    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.626                                                 
## PopulatinKA -0.698  0.437                                          
## PopulatinMA -0.697  0.437  0.487                                   
## PopulatinMU -0.707  0.443  0.493  0.493                            
## PopulatinRE -0.640  0.401  0.447  0.446  0.452                     
## PopulatinUM -0.654  0.410  0.457  0.456  0.462  0.418              
## PopulatinVA -0.678  0.424  0.473  0.472  0.479  0.434  0.443       
## PopulatinYE -0.706  0.442  0.493  0.492  0.499  0.452  0.462  0.478

Tergite10

anova(Pgm2_lmer_Schmidt_Tergite10)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 78.149  9.7686     8 148.36  2.3141 0.02283 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm2_lmer_Schmidt_Tergite10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Tergite10 ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm2, Supervisor.PI == "Schmidt"))
## 
## REML criterion at convergence: 6832.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7236 -0.6439 -0.1671  0.6539  3.2399 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 4.184    2.045   
##  Residual                    4.221    2.055   
## Number of obs: 1514, groups:  Line:Population, 157
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    2.1450     0.4799 146.8229   4.470 1.56e-05 ***
## PopulationGI   0.1159     0.7671 148.6520   0.151 0.880056    
## PopulationKA   1.4287     0.6875 146.8229   2.078 0.039454 *  
## PopulationMA   1.6307     0.6888 147.7582   2.368 0.019197 *  
## PopulationMU   0.8444     0.6790 147.0910   1.244 0.215633    
## PopulationRE   0.9257     0.7511 149.2376   1.232 0.219716    
## PopulationUM   2.6361     0.7339 147.4353   3.592 0.000446 ***
## PopulationVA   1.6089     0.7082 147.0280   2.272 0.024555 *  
## PopulationYE   1.2194     0.6803 148.2432   1.792 0.075122 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.626                                                 
## PopulatinKA -0.698  0.437                                          
## PopulatinMA -0.697  0.436  0.486                                   
## PopulatinMU -0.707  0.442  0.493  0.492                            
## PopulatinRE -0.639  0.400  0.446  0.445  0.452                     
## PopulatinUM -0.654  0.409  0.456  0.456  0.462  0.418              
## PopulatinVA -0.678  0.424  0.473  0.472  0.479  0.433  0.443       
## PopulatinYE -0.705  0.441  0.492  0.491  0.499  0.451  0.461  0.478

Total

anova(Pgm2_lmer_Schmidt_Total)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Population 232.51  29.064     8 148.08  2.1415 0.03533 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm2_lmer_Schmidt_Total)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Total ~ Population + (1 | Line:Population)
##    Data: (subset(d_Pgm2, Supervisor.PI == "Schmidt"))
## 
## REML criterion at convergence: 8630.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7804 -0.6403 -0.1225  0.6108  4.0733 
## 
## Random effects:
##  Groups          Name        Variance Std.Dev.
##  Line:Population (Intercept) 18.12    4.257   
##  Residual                    13.57    3.684   
## Number of obs: 1514, groups:  Line:Population, 157
## 
## Fixed effects:
##              Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    8.9600     0.9869 146.8837   9.079 6.73e-16 ***
## PopulationGI  -0.5480     1.5763 148.3022  -0.348  0.72860    
## PopulationKA   2.1400     1.4140 146.8837   1.513  0.13231    
## PopulationMA   3.1858     1.4159 147.6323   2.250  0.02593 *  
## PopulationMU   1.1931     1.3962 147.0911   0.855  0.39420    
## PopulationRE   2.0855     1.5431 148.7609   1.351  0.17859    
## PopulationUM   4.9263     1.5088 147.3616   3.265  0.00136 ** 
## PopulationVA   1.2988     1.4564 147.0412   0.892  0.37398    
## PopulationYE   1.5982     1.3984 147.9763   1.143  0.25492    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.626                                                 
## PopulatinKA -0.698  0.437                                          
## PopulatinMA -0.697  0.436  0.487                                   
## PopulatinMU -0.707  0.443  0.493  0.493                            
## PopulatinRE -0.640  0.400  0.446  0.446  0.452                     
## PopulatinUM -0.654  0.410  0.457  0.456  0.462  0.418              
## PopulatinVA -0.678  0.424  0.473  0.472  0.479  0.433  0.443       
## PopulatinYE -0.706  0.442  0.493  0.492  0.499  0.451  0.462  0.478

Lab correlations

There are 9 populations and 149 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.

Results II : Meta-Analyses tbd

Viability

Development Time

Females

Males

Dry weight

Females

Males

Lifespan

Females

Males

Results III : Environmental Variables

Geography - Latitude

Viability

Gibert Lab

anova(Via_lmer_Lat_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)  
## Latitude 0.068572 0.068572     1 7.1226   5.521 0.0505 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Grath Lab

anova(Via_lmer_Lat_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.047793 0.047793     1 1.0297  2.3848 0.3607

Hoedjes Lab

anova(Via_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Latitude 0.14003 0.14003     1 7.3684  13.595 0.007111 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Schmidt Lab

anova(Via_lmer_Lat_Schmidt) 
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.14657 0.14657     1 6.7226   1.793 0.2241

Stamenkovic-Radak Lab

anova(Via_lmer_Lat_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.019734 0.019734     1 7.1768  1.6379 0.2404

Zwaan Lab

anova(Via_lmer_Lat_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 0.32986 0.32986     1 7.0542  9.9389 0.01593 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Developmental time

dir.create(file.path("DevelopmentTime"), showWarnings = FALSE)

Egg-to-pupa developmental time

Schmidt Lab
anova(DT_P_lmer_Lat)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 148.08  148.08     1 6.9929  1.2438 0.3016

Egg-to-adult developmental time

Gibert Lab
anova(DT_A_F_lmer_Lat_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 4.9192  4.9192     1 6.8757  0.0506 0.8285
anova(DT_A_M_lmer_Lat_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude  2.426   2.426     1 6.7608  0.0222 0.8859
Grath Lab
anova(DT_A_F_lmer_Lat_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 152.75  152.75     1 1.0154   0.486  0.611
anova(DT_A_M_lmer_Lat_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 40.186  40.186     1 1.0316  0.1335 0.7755
Hoedjes Lab
anova(DT_A_F_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 1046.5  1046.5     1 6.8151   6.746 0.03641 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_M_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 1193.7  1193.7     1 6.8075  5.7584 0.04847 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Schmidt Lab
anova(DT_A_F_lmer_Lat_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 12.366  12.366     1 7.3289   0.057 0.8178
anova(DT_A_M_lmer_Lat_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.04256 0.04256     1 6.9309   1e-04 0.9908
Stamenkovic-Radak Lab
anova(DT_A_F_lmer_Lat_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 353.65  353.65     1 7.1388  0.8847 0.3776
anova(DT_A_M_lmer_Lat_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 309.45  309.45     1 7.1523  0.7254  0.422
Zwaan Lab
anova(DT_A_F_lmer_Lat_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 101.86  101.86     1 6.9315  0.8691 0.3825
anova(DT_A_M_lmer_Lat_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 22.846  22.846     1 6.6572  0.1783 0.6862

Dry weight

Colinet Lab

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0024786 (tol = 0.002, component 1)
anova(DW_F_lmer_Lat_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.0010313 0.0010313     1 6.8158  0.3827 0.5563
anova(DW_M_lmer_Lat_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.00022165 0.00022165     1 7.028  0.3095 0.5952

Hoedjes Lab

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00318688 (tol = 0.002, component 1)
anova(DW_F_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.00018731 0.00018731     1 7.1001  0.0829 0.7816
## boundary (singular) fit: see ?isSingular
anova(DW_M_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.00035052 0.00035052     1 164.94  0.5114 0.4755

Onder Lab

anova(DW_F_lmer_Lat_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 5.1001e-05 5.1001e-05     1 7.0236  0.0192 0.8937
anova(DW_M_lmer_Lat_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.0001985 0.0001985     1 6.873  0.2161 0.6564

Thorax length

Kozeretska Lab

anova(TL_F_lmer_Lat_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 2090.9  2090.9     1 6.6825  0.8191 0.3969
anova(TL_M_lmer_Lat_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 922.58  922.58     1 6.7735    0.41  0.543

Posnien Lab

anova(TL_F_lmer_Lat_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 2549.8  2549.8     1     7  1.6586 0.2387
anova(TL_M_lmer_Lat_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1361.8  1361.8     1     7  1.1207 0.3249

Ritchie Lab

## boundary (singular) fit: see ?isSingular
anova(TL_F_lmer_Lat_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 6315.9  6315.9     1 23.94  2.2425 0.1473
## boundary (singular) fit: see ?isSingular
anova(TL_M_lmer_Lat_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 2407.5  2407.5     1 24.045  0.6726 0.4202

Schmidt Lab

anova(TL_F_lmer_Lat_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 7587.3  7587.3     1 6.6722  6.0178 0.04557 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Wing area

Left

Onder Lab

anova(WA_Lat_F_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 7896.8  7896.8     1 6.9295  1.2238 0.3055
anova(WA_Lat_M_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 3135.5  3135.5     1 6.9311  0.5964 0.4655

Posnien Lab

## boundary (singular) fit: see ?isSingular
anova(WA_Lat_F_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
## Latitude  54803   54803     1    25  6.5631 0.01682 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(WA_Lat_M_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude  11392   11392     1    25  1.9002 0.1803

Ritchie Lab

anova(WA_Lat_F_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 174.22  174.22     1 7.826  0.0134 0.9106
anova(WA_Lat_M_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 24.429  24.429     1 7.7198  0.0025 0.9615

Stamenkovic-Radak Lab

anova(WA_Lat_F_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude  26336   26336     1 6.8837   3.402 0.1084
anova(WA_Lat_M_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude  22919   22919     1 6.9146  3.8312 0.09169 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Right

Onder Lab

anova(WA_Lat_F_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 7711.5  7711.5     1 6.9316  1.1933 0.3112
anova(WA_Lat_M_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 3176.2  3176.2     1 6.9296  0.5972 0.4652

Posnien Lab

## boundary (singular) fit: see ?isSingular
anova(WA_Lat_F_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
## Latitude  57489   57489     1    25  6.6128 0.01646 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(WA_Lat_M_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude  11736   11736     1    25  1.9456 0.1753

Ritchie Lab

anova(WA_Lat_F_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 153.16  153.16     1 7.7755  0.0117 0.9167
anova(WA_Lat_M_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.040336 0.040336     1 7.6741       0 0.9984

Stamenkovic-Radak Lab

anova(WA_Lat_F_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude  27172   27172     1 6.8816  3.5256 0.1032
anova(WA_Lat_M_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
## Latitude  21431   21431     1 6.908  3.6506 0.09822 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fecundity

dir.create(file.path("Fecundity"), showWarnings = FALSE)

Billeter Lab

anova(Fec_lmer_Lat_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude  18138   18138     1 6.8508   6.565 0.03812 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fricke Lab

## boundary (singular) fit: see ?isSingular
anova(Fec_lmer_Lat_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 215.56  215.56     1 162.2  0.1708 0.6799

Lifespan

dir.create(file.path("Lifespan"), showWarnings = FALSE)

Flatt Lab

anova(LS_Lat_P_F_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Latitude 2894.9  2894.9     1 7.0035  17.915 0.003871 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(LS_Lat_P_M_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 140.55  140.55     1 7.004  0.7886  0.404

Parsch Lab

anova(LS_Lat_L_F_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 1161.9  1161.9     1 6.9763  3.7271 0.09498 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(LS_Lat_L_M_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## Latitude 6667.5  6667.5     1 7.0283  18.136 0.003719 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pasyukova Lab

anova(LS_Lat_L_F_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 289.98  289.98     1 6.7697  1.1771 0.3151
anova(LS_Lat_L_M_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 1160.8  1160.8     1 6.9458  3.5886 0.1004

Cold-shock mortality

Gonzalez Lab

anova(CSM_F_lmer_Lat_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.066037 0.066037     1 6.6147  1.4592 0.2685
anova(CSM_M_lmer_Lat_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.091607 0.091607     1 6.2138  1.0344 0.3471

Kozeretska Lab

## boundary (singular) fit: see ?isSingular
anova(CSM_F_lmer_Lat_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.037639 0.037639     1 121.44  0.2851 0.5944
## boundary (singular) fit: see ?isSingular
anova(CSM_M_lmer_Lat_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.049652 0.049652     1 120.96  0.3545 0.5527

Vieira Lab

anova(CSM_F_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.039207 0.039207     1 7.3591  1.3043 0.2892
anova(CSM_M_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.037822 0.037822     1 7.1265  1.0164 0.3464

Chill-coma recovery time

dir.create(file.path("ChillComa"), showWarnings = FALSE)
anova(CCRT_F_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 61.122  61.122     1 7.122   3e-04 0.9859
anova(CCRT_M_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 290488  290488     1 6.8661  1.8478  0.217

Mensh Lab

Heat-shock mortality

For a detailed description of tables, plots, linear models and outputs, please refer here

Parsch Lab

anova(HSM_F_lmer_Lat_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude  35232   35232     1 7.0013  5.3708 0.05358 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(HSM_M_lmer_Lat_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude  52510   52510     1 7.0012  9.8787 0.01631 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Vieira Lab

anova(HSM_F_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 156.27  156.27     1  6.96  0.0513 0.8274
anova(HSM_M_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 4723.9  4723.9     1 6.5508  1.5857  0.251

Diapause

Bergland Lab

anova(Dia_lmer_Lat_Bergland)

Flatt Lab

anova(Dia_lmer_Lat_Flatt)

Schlotterer Lab

anova(Dia_lmer_Lat_Schlotterer)

Circadian eclosion timing

Locomotor activity

Tauber Lab

anova(LA_lmer_Lat_ND_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.037349 0.037349     1 7.9016  0.2526  0.629

CircPhase

anova(LA_lmer_Lat_CircPhase_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 61.725  61.725     1 7.2616  1.3242 0.2863

Activity

anova(LA_lmer_Lat_Activity_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 85.098  85.098     1 6.779  1.1785 0.3148

Starvation resistance

Gonzalez Lab

anova(SR_F_lmer_Lat_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 2767.2  2767.2     1 7.2849  6.4834 0.03707 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SR_M_lmer_Lat_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 1281.4  1281.4     1 7.2192  5.8942 0.04453 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Onder Lab

anova(SR_F_lmer_Lat_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 1576.1  1576.1     1 7.2393  4.8876 0.06147 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SR_M_lmer_Lat_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Latitude 764.76  764.76     1 7.2317  3.8985 0.08759 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pasyukova Lab

anova(SR_F_lmer_Lat_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 874.85  874.85     1 6.8839    1.82   0.22
anova(SR_M_lmer_Lat_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 707.52  707.52     1 7.1623  2.5014 0.1568

Pigmentation

Abbott Lab

PercT4

anova(Pgm_lmer_Lat_Abbott_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 63.659  63.659     1 6.988  2.0598 0.1944

PercT5

anova(Pgm_lmer_Lat_Abbott_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 40.645  40.645     1 7.0027  1.1746 0.3144

PercT6

anova(Pgm_lmer_Lat_Abbott_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 50.979  50.979     1 6.8993  0.3184 0.5904

TotalPerc

anova(Pgm_lmer_Lat_Abbott_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude  39.59   39.59     1 6.969  1.1571 0.3179

Gibert Lab

PercT4

anova(Pgm_lmer_Lat_Gibert_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 6.3924  6.3924     1 6.8915  0.0706 0.7983

PercT5

anova(Pgm_lmer_Lat_Gibert_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.043179 0.043179     1 6.9001   4e-04 0.9844

PercT6

anova(Pgm_lmer_Lat_Gibert_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 3.6662  3.6662     1 7.0273  0.0176  0.898

TotalPerc

## boundary (singular) fit: see ?isSingular
anova(Pgm_lmer_Lat_Gibert_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.50455 0.50455     1 165.34  0.0069 0.9341

Schmidt Lab

Tergite8

anova(Pgm2_lmer_Lat_Schmidt_Tergite8)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.38967 0.38967     1 6.7828  0.5421 0.4862

Tergite9

anova(Pgm2_lmer_Lat_Schmidt_Tergite9)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.19172 0.19172     1 5.8379  0.0752 0.7933

Tergite10

anova(Pgm2_lmer_Lat_Schmidt_Tergite10)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 0.019915 0.019915     1 6.5722  0.0047 0.9473

Total

anova(Pgm2_lmer_Lat_Schmidt_Total)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Latitude 1.2739  1.2739     1 6.2808  0.0939 0.7692

Geography - Longitude

Viability

Gibert Lab

anova(Via_lmer_Long_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.0010065 0.0010065     1 7.1692   0.081 0.7839

Grath Lab

anova(Via_lmer_Long_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.013299 0.013299     1 1.0042  0.6636 0.5644

Hoedjes Lab

anova(Via_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.00087846 0.00087846     1 7.2565  0.0853 0.7784

Schmidt Lab

anova(Via_lmer_Long_Schmidt) 
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.00078423 0.00078423     1 7.2723  0.0096 0.9246

Stamenkovic-Radak Lab

anova(Via_lmer_Long_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.017496 0.017496     1 7.4444  1.4521 0.2651

Zwaan Lab

anova(Via_lmer_Long_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0042991 0.0042991     1 7.178  0.1296 0.7292

Developmental time

dir.create(file.path("DevelopmentTime"), showWarnings = FALSE)

Egg-to-pupa developmental time

Schmidt Lab
anova(DT_P_lmer_Long)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.057398 0.057398     1 7.3594   5e-04 0.9831

Egg-to-adult developmental time

Gibert Lab
anova(DT_A_F_lmer_Long_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Longitude 442.95  442.95     1 7.5199  4.5593 0.06743 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_M_lmer_Long_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 142.01  142.01     1 7.1837  1.3004 0.2907
Grath Lab
DT_A_F_lmer_Long_Grath <- lmer(DT_EggAdult ~ Longitude + (1|Population) + (1|Line:Population), data = d_DT_A_F[d_DT_A_F$Supervisor.PI == "Grath",])
capture.output(summary(DT_A_F_lmer_Long_Grath),file = "DevelopmentTime/DT_A_F_lmer_Long_Grath_sum.txt")
capture.output(anova(DT_A_F_lmer_Long_Grath),file = "DevelopmentTime/DT_A_F_lmer_Long_Grath.txt")
anova(DT_A_F_lmer_Long_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  520.6   520.6     1 1.1943  1.6566 0.3932
anova(DT_A_M_lmer_Long_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 174.67  174.67     1 1.3194  0.5804 0.5587
Hoedjes Lab
anova(DT_A_F_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Longitude 823.09  823.09     1 7.3767  5.3056 0.05287 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
DT_A_M_lmer_Long_Hoedjes <- lmer(DT_EggAdult ~ Longitude + (1|Population) + (1|Line:Population) ,data = d_DT_A_M[d_DT_A_M$Supervisor.PI == "Hoedjes",])
capture.output(summary(DT_A_M_lmer_Long_Hoedjes),file = "DevelopmentTime/DT_A_M_lmer_Long_Hoedjes_sum.txt")
capture.output(anova(DT_A_M_lmer_Long_Hoedjes),file = "DevelopmentTime/DT_A_M_lmer_Long_Hoedjes.txt")
anova(DT_A_M_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Longitude 1180.2  1180.2     1 7.4073  5.6936 0.04653 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Schmidt Lab
anova(DT_A_F_lmer_Long_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 29.616  29.616     1 7.7873  0.1366 0.7215
DT_A_M_lmer_Long_Schmidt <- lmer(DT_EggAdult ~ Longitude + (1|Population) + (1|Line:Population), data = d_DT_A_M[d_DT_A_M$Supervisor.PI == "Schmidt",])
capture.output(summary(DT_A_M_lmer_Long_Schmidt),file = "DevelopmentTime/DT_A_M_lmer_Long_Schmidt_sum.txt")
capture.output(anova(DT_A_M_lmer_Long_Schmidt),file = "DevelopmentTime/DT_A_M_lmer_Long_Schmidt.txt")
anova(DT_A_M_lmer_Long_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 17.638  17.638     1 7.3191  0.0594 0.8142
Stamenkovic-Radak Lab
anova(DT_A_F_lmer_Long_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 1.3037  1.3037     1 7.2564  0.0033  0.956
anova(DT_A_M_lmer_Long_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  5.495   5.495     1 7.2605  0.0129 0.9127
Zwaan Lab
anova(DT_A_F_lmer_Long_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Longitude 410.18  410.18     1 8.1104  3.4997 0.09779 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_M_lmer_Long_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Longitude 1001.8  1001.8     1 8.2231  7.8178 0.02272 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Dry weight

Colinet Lab

anova(DW_F_lmer_Long_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.0025578 0.0025578     1 7.3126  0.9491 0.3611
anova(DW_M_lmer_Long_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.00087809 0.00087809     1 7.2443  1.2263 0.3035

Hoedjes Lab

anova(DW_F_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.00068713 0.00068713     1 7.8502  0.3041 0.5967
## boundary (singular) fit: see ?isSingular
anova(DW_M_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.00041152 0.00041152     1 164.84  0.6004 0.4395

Onder Lab

anova(DW_F_lmer_Long_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq    Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.00013742 0.00013742     1 7.207  0.0517 0.8265
anova(DW_M_lmer_Long_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 1.7215e-05 1.7215e-05     1 7.2064  0.0187 0.8949

Thorax length

dir.create(file.path("ThoraxLength"), showWarnings = FALSE)

Kozeretska Lab

anova(TL_F_lmer_Long_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 51.134  51.134     1 7.2101    0.02 0.8913
anova(TL_M_lmer_Long_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 110.19  110.19     1 7.2587   0.049  0.831

Posnien Lab

anova(TL_F_lmer_Long_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 26.558  26.558     1 6.9994  0.0173 0.8991
anova(TL_M_lmer_Long_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 55.595  55.595     1 7.0004  0.0458 0.8367

Ritchie Lab

## boundary (singular) fit: see ?isSingular
anova(TL_F_lmer_Long_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 1603.5  1603.5     1 23.897  0.5693 0.4579
TL_M_lmer_Long_Ritchie <- lmer(TL_micrometers ~ Longitude + (1|Population) + (1|Line:Population), data = (subset(d_TL_M,Supervisor.PI=='Ritchie')))
## boundary (singular) fit: see ?isSingular
capture.output(summary(TL_M_lmer_Long_Ritchie),file = "ThoraxLength/TL_M_Ritchie_sum.txt")
capture.output(anova(TL_M_lmer_Long_Ritchie),file = "ThoraxLength/TL_M_lmer_Long_Ritchie.txt")
anova(TL_M_lmer_Long_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude   3131    3131     1 24.071  0.8747 0.3589

Schmidt Lab

TL_F_lmer_Long_Schmidt <- lmer(TL_micrometers ~ Longitude + (1|Population) + (1|Line:Population), data = (subset(d_TL_F,Supervisor.PI=='Schmidt')))
capture.output(summary(TL_F_lmer_Long_Schmidt),file = "ThoraxLength/TL_F_Schmidt_sum.txt")
capture.output(anova(TL_F_lmer_Long_Schmidt),file = "ThoraxLength/TL_F_lmer_Long_Schmidt.txt")
anova(TL_F_lmer_Long_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Longitude 5017.8  5017.8     1 7.7441  3.9791 0.08235 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Wing area

Left

Onder Lab

anova(WA_Long_F_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  21030   21030     1 7.0413  3.2589 0.1138
anova(WA_Long_M_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  11117   11117     1 7.0742  2.1144 0.1888

Posnien Lab

anova(WA_Long_F_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)  
## Longitude  31435   31435     1 7.0004  3.7646 0.0935 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(WA_Long_M_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 4259.1  4259.1     1    25  0.7104 0.4073

Ritchie Lab

anova(WA_Long_F_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 5945.6  5945.6     1 7.3399  0.4589 0.5189
anova(WA_Long_M_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 57.349  57.349     1 7.0233  0.0058 0.9412

Stamenkovic-Radak Lab

anova(WA_Long_F_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  13008   13008     1 7.1043  1.6804 0.2354
anova(WA_Long_M_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 9380.6  9380.6     1 7.1449  1.5681 0.2499

Right

anova(WA_Long_F_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  21347   21347     1 7.0421  3.3033 0.1117
anova(WA_Long_M_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  12027   12027     1 7.0764  2.2614 0.1759

Posnien Lab

anova(WA_Long_F_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Longitude  31861   31861     1 7.0001  3.6649 0.09713 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(WA_Long_M_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 4851.5  4851.5     1 24.999  0.8043 0.3784

Ritchie Lab

anova(WA_Long_F_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 5768.1  5768.1     1 7.2969  0.4393 0.5278
anova(WA_Long_M_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 6.5633  6.5633     1 7.0183   7e-04   0.98

Stamenkovic-Radak Lab

anova(WA_Long_F_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  12085   12085     1 7.0993  1.5681 0.2502
anova(WA_Long_M_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 8262.2  8262.2     1 7.1372  1.4074 0.2735

Fecundity

dir.create(file.path("Fecundity"), showWarnings = FALSE)

Billeter Lab

anova(Fec_lmer_Long_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 594.33  594.33     1 7.519  0.2151  0.656

Fricke Lab

## boundary (singular) fit: see ?isSingular
anova(Fec_lmer_Long_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 168.63  168.63     1 162.13  0.1336 0.7152

Lifespan

Flatt Lab

anova(LS_Long_P_F_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 419.22  419.22     1 6.9999  2.5943 0.1513
anova(LS_Long_P_M_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 12.113  12.113     1 6.9904   0.068 0.8018

Parsch Lab

anova(LS_Long_L_F_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 332.72  332.72     1 6.9913  1.0673  0.336
anova(LS_Long_L_M_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 1095.2  1095.2     1 6.9951  2.9789  0.128

Pasyukova Lab

anova(LS_Long_L_F_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 623.22  623.22     1 7.6402  2.5297 0.1522
anova(LS_Long_L_M_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 341.74  341.74     1 7.3639  1.0565 0.3366

Cold-shock mortality

dir.create(file.path("ColdShock"), showWarnings = FALSE)

Gonzalez Lab

anova(CSM_F_lmer_Long_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.0079988 0.0079988     1 7.1065  0.1768 0.6865
anova(CSM_M_lmer_Long_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.0001445 0.0001445     1 7.5298  0.0016 0.9688

Kozeretska Lab

## boundary (singular) fit: see ?isSingular
anova(CSM_F_lmer_Long_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.016849 0.016849     1 114.22  0.1274 0.7218
## boundary (singular) fit: see ?isSingular
anova(CSM_M_lmer_Long_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.0042714 0.0042714     1 113.22  0.0305 0.8617

Vieira Lab

anova(CSM_F_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.0064623 0.0064623     1 7.8424   0.215 0.6555
anova(CSM_M_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.0079713 0.0079713     1 7.6665  0.2142 0.6563

Chill-coma recovery time

anova(CCRT_F_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude  90246   90246     1 7.291  0.4922 0.5047
anova(CCRT_M_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  41945   41945     1 7.0115  0.2668 0.6214

Mensh Lab

Heat-shock mortality

For a detailed description of tables, plots, linear models and outputs, please refer here

Parsch Lab

anova(HSM_F_lmer_Long_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 2583.4  2583.4     1 7.0335  0.3938 0.5501
anova(HSM_M_lmer_Long_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 4076.2  4076.2     1 7.0029  0.7668 0.4102

Vieira Lab

anova(HSM_F_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 137.31  137.31     1 7.0495  0.0451 0.8379
anova(HSM_M_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 1496.3  1496.3     1 6.7623  0.5023 0.5022

Diapause

Bergland Lab

anova(Dia_lmer_Long_Bergland)

Flatt Lab

anova(Dia_lmer_Long_Flatt)

Schlotterer Lab

anova(Dia_lmer_Long_Schlotterer)

Circadian eclosion timing

dir.create(file.path("CircadianEclosion"), showWarnings = FALSE)

Locomotor activity

dir.create(file.path("Locomotor"), showWarnings = FALSE)

Tauber Lab

ND

anova(LA_lmer_Long_ND_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.056798 0.056798     1 8.8887  0.3841  0.551

CircPhase

LA_lmer_Long_CircPhase_Tauber <- lmer(CircPhase ~ Longitude + (1|Population)  + (1|Line:Population),data = (d_LA))
capture.output(summary(LA_lmer_Long_CircPhase_Tauber),file = "Locomotor/LA_lmer_Long_CircPhase_Tauber_sum.txt")
capture.output(anova(LA_lmer_Long_CircPhase_Tauber),file = "Locomotor/LA_lmer_Long_CircPhase_Tauber.txt")
anova(LA_lmer_Long_CircPhase_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 49.877  49.877     1 7.7857  1.0684 0.3323

Activity

anova(LA_lmer_Long_Activity_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 189.68  189.68     1 7.3222  2.6277 0.1472

Starvation resistance

Gonzalez Lab

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00313532 (tol = 0.002, component 1)
anova(SR_F_lmer_Long_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 43.103  43.103     1 7.5547   0.101 0.7593
anova(SR_M_lmer_Long_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.53909 0.53909     1 7.2918  0.0025 0.9616

Onder Lab

anova(SR_F_lmer_Long_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude  10.72   10.72     1 7.2773  0.0332 0.8603
anova(SR_M_lmer_Long_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 18.713  18.713     1 7.372  0.0954  0.766

Pasyukova Lab

anova(SR_F_lmer_Long_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 285.73  285.73     1 7.3486  0.5944 0.4648
anova(SR_M_lmer_Long_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 29.939  29.939     1 7.2776  0.1058 0.7541

Pigmentation

Abbott Lab

PercT4

anova(Pgm_lmer_Long_Abbott_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 63.659  63.659     1 6.988  2.0598 0.1944

PercT5

anova(Pgm_lmer_Long_Abbott_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 40.645  40.645     1 7.0027  1.1746 0.3144

PercT6

Pgm_lmer_Long_Abbott_PercT6 <- lmer(PercT6 ~ Longitude + (1|Population)  + (1|Line:Population),data = (subset(d_Pgm,Supervisor.PI=='Abbott')))
capture.output(summary(Pgm_lmer_Long_Abbott_PercT6),file = "Pigmentation/Pgm_lmer_Long_Abbott_PercT6_sum.txt")
capture.output(anova(Pgm_lmer_Long_Abbott_PercT6),file = "Pigmentation/Pgm_lmer_Long_Abbott_PercT6.txt")
anova(Pgm_lmer_Long_Abbott_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 50.979  50.979     1 6.8993  0.3184 0.5904

TotalPerc

anova(Pgm_lmer_Long_Abbott_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude  39.59   39.59     1 6.969  1.1571 0.3179

Gibert Lab

PercT4

anova(Pgm_lmer_Long_Gibert_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 6.3924  6.3924     1 6.8915  0.0706 0.7983

PercT5

anova(Pgm_lmer_Long_Gibert_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.043179 0.043179     1 6.9001   4e-04 0.9844

PercT6

anova(Pgm_lmer_Long_Gibert_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 3.6662  3.6662     1 7.0273  0.0176  0.898

TotalPerc

## boundary (singular) fit: see ?isSingular
anova(Pgm_lmer_Long_Gibert_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.50455 0.50455     1 165.34  0.0069 0.9341

Schmidt Lab

Tergite8

anova(Pgm2_lmer_Long_Schmidt_Tergite8)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.050033 0.050033     1 7.2265  0.0696 0.7993

Tergite9

anova(Pgm2_lmer_Long_Schmidt_Tergite9)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 0.17477 0.17477     1 6.9364  0.0686  0.801

Tergite10

anova(Pgm2_lmer_Long_Schmidt_Tergite10)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 6.2458  6.2458     1 7.7863  1.4796 0.2594

Total

anova(Pgm2_lmer_Long_Schmidt_Total)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Longitude 5.2988  5.2988     1 7.2798  0.3904 0.5511

Geography - Altitude

Viability

dir.create(file.path("Viability"), showWarnings = FALSE)

Gibert Lab

anova(Via_lmer_Alt_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.041186 0.041186     1 6.8919   3.316 0.1121

Grath Lab

anova(Via_lmer_Alt_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF   DenDF F value Pr(>F)
## Altitude 0.035661 0.035661     1 0.99521  1.7797 0.4103

Hoedjes Lab

anova(Via_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.0092781 0.0092781     1 6.8246  0.9008  0.375

Schmidt Lab

anova(Via_lmer_Alt_Schmidt) 
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.11234 0.11234     1 6.2853  1.3732 0.2838

Stamenkovic-Radak Lab

anova(Via_lmer_Alt_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.027819 0.027819     1 7.0405   2.309 0.1722

Zwaan Lab

anova(Via_lmer_Alt_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.1106  0.1106     1 6.7736  3.3331 0.1121

Developmental time

dir.create(file.path("DevelopmentTime"), showWarnings = FALSE)

Egg-to-pupa developmental time

Schmidt Lab
anova(DT_P_lmer_Alt)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 12.886  12.886     1 6.4564  0.1082 0.7526

Egg-to-adult developmental time

Gibert Lab
anova(DT_A_F_lmer_Alt_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Altitude 394.07  394.07     1 6.6617   4.056 0.08594 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_M_lmer_Alt_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 388.62  388.62     1 6.4921  3.5587 0.1045
Grath Lab
anova(DT_A_F_lmer_Alt_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF   DenDF F value Pr(>F)
## Altitude 130.78  130.78     1 0.88974  0.4161 0.6457
anova(DT_A_M_lmer_Alt_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF   DenDF F value Pr(>F)
## Altitude 333.57  333.57     1 0.62544  1.1083 0.5539
Hoedjes Lab
anova(DT_A_F_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 13.445  13.445     1 6.6617  0.0867 0.7774
anova(DT_A_M_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 7.4907  7.4907     1 6.6463  0.0361 0.8549
Schmidt Lab
anova(DT_A_F_lmer_Alt_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 11.642  11.642     1 7.0463  0.0537 0.8233
DT_A_M_lmer_Alt_Schmidt <- lmer(DT_EggAdult ~ Altitude + (1|Population) + (1|Line:Population), data = d_DT_A_M[d_DT_A_M$Supervisor.PI == "Schmidt",])
capture.output(summary(DT_A_M_lmer_Alt_Schmidt),file = "DevelopmentTime/DT_A_M_lmer_Alt_Schmidt_sum.txt")
capture.output(anova(DT_A_M_lmer_Alt_Schmidt),file = "DevelopmentTime/DT_A_M_lmer_Alt_Schmidt.txt")
anova(DT_A_M_lmer_Alt_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 28.785  28.785     1 6.7078  0.0969 0.7651
Stamenkovic-Radak Lab
anova(DT_A_F_lmer_Alt_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 6.1328  6.1328     1 6.9047  0.0153  0.905
anova(DT_A_M_lmer_Alt_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 10.826  10.826     1 6.9162  0.0254  0.878
Zwaan Lab
anova(DT_A_F_lmer_Alt_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 42.459  42.459     1 6.6749  0.3623 0.5671
anova(DT_A_M_lmer_Alt_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 217.23  217.23     1 6.4051  1.6952 0.2378

Dry weight

Colinet Lab

anova(DW_F_lmer_Alt_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.00058901 0.00058901     1 6.5839  0.2186 0.6552
anova(DW_M_lmer_Alt_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.0010067 0.0010067     1 6.8721  1.4059 0.2751

Hoedjes Lab

anova(DW_F_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.0019488 0.0019488     1 6.8061  0.8625 0.3848
## boundary (singular) fit: see ?isSingular
anova(DW_M_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.00079782 0.00079782     1 164.91  1.1641 0.2822

Onder Lab

anova(DW_F_lmer_Alt_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.00039668 0.00039668     1 6.9485  0.1492 0.7109
DW_M_lmer_Alt_Onder <- lmer(DW_micrograms ~ Altitude + (1|Population) + (1|Population:Line),data = d_DW_M[d_DW_M$Supervisor.PI == "Onder",])
capture.output(summary(DW_M_lmer_Alt_Onder),file = "DryWeight/DW_M_lmer_Alt_Onder_sum.txt")
capture.output(anova(DW_M_lmer_Alt_Onder),file = "DryWeight/DW_M_lmer_Alt_Onder.txt")
anova(DW_M_lmer_Alt_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 3.1127e-05 3.1127e-05     1 6.6562  0.0339 0.8594

Thorax length

Kozeretska Lab

anova(TL_F_lmer_Alt_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 7428.9  7428.9     1 6.6794  2.9103 0.1338
anova(TL_M_lmer_Alt_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 4631.5  4631.5     1 6.8648  2.0583 0.1953

Posnien Lab

anova(TL_F_lmer_Alt_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 186.88  186.88     1     7  0.1216 0.7376
anova(TL_M_lmer_Alt_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 841.27  841.27     1 7.0004  0.6923 0.4328

Ritchie Lab

## boundary (singular) fit: see ?isSingular
anova(TL_F_lmer_Alt_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Altitude 8529.6  8529.6     1 23.916  3.0285 0.09467 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(TL_M_lmer_Alt_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 8644.9  8644.9     1 24.071  2.4152 0.1332

Schmidt Lab

anova(TL_F_lmer_Alt_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.097583 0.097583     1 6.439   1e-04 0.9932

Wing area

dir.create(file.path("WingArea"), showWarnings = FALSE)

Left

Onder Lab

anova(WA_Alt_F_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 342.29  342.29     1 6.8913   0.053 0.8245
anova(WA_Alt_M_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 800.11  800.11     1 6.8758  0.1522 0.7083

Posnien Lab

anova(WA_Alt_F_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 427.13  427.13     1     7  0.0512 0.8275
## boundary (singular) fit: see ?isSingular
anova(WA_Alt_M_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 131.41  131.41     1    25  0.0219 0.8835

Ritchie Lab

anova(WA_Alt_F_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude   5599    5599     1 7.9061  0.4321 0.5296
anova(WA_Alt_M_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude  99.14   99.14     1 7.1968  0.0101 0.9227

Stamenkovic-Radak Lab

anova(WA_Alt_F_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 500.23  500.23     1 7.0147  0.0646 0.8066
anova(WA_Alt_M_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 453.74  453.74     1 7.0222  0.0758 0.7909

Right

anova(WA_Alt_F_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 429.92  429.92     1 6.8921  0.0665  0.804
anova(WA_Alt_M_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 975.76  975.76     1 6.8726  0.1835 0.6815

Posnien Lab

anova(WA_Alt_F_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 306.55  306.55     1 6.9999  0.0353 0.8564
## boundary (singular) fit: see ?isSingular
anova(WA_Alt_M_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 30.438  30.438     1    25   0.005 0.9439

Ritchie Lab

anova(WA_Alt_F_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 6054.3  6054.3     1 7.8555   0.461 0.5167
anova(WA_Alt_M_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 48.386  48.386     1 7.1818   0.005 0.9456

Stamenkovic-Radak Lab

anova(WA_Alt_F_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 637.13  637.13     1 7.0173  0.0827  0.782
anova(WA_Alt_M_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 439.77  439.77     1 7.0227  0.0749 0.7922

Fecundity

dir.create(file.path("Fecundity"), showWarnings = FALSE)

Billeter Lab

anova(Fec_lmer_Alt_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 2814.1  2814.1     1 6.3615  1.0185 0.3497

Fricke Lab

## boundary (singular) fit: see ?isSingular
anova(Fec_lmer_Alt_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 134.07  134.07     1 157.2  0.1062 0.7449

Lifespan

dir.create(file.path("Lifespan"), showWarnings = FALSE)

Flatt Lab

anova(LS_Alt_P_F_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude  13.92   13.92     1 7.0003  0.0861 0.7777
anova(LS_Alt_P_M_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 20.883  20.883     1 6.9979  0.1172 0.7422

Parsch Lab

anova(LS_Alt_L_F_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 199.34  199.34     1 7.0096  0.6394 0.4502
anova(LS_Alt_L_M_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 287.14  287.14     1 7.007   0.781 0.4061

Pasyukova Lab

anova(LS_Alt_L_F_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 344.59  344.59     1 6.6393  1.3987 0.2776
anova(LS_Alt_L_M_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 664.65  664.65     1 6.8038  2.0548  0.196

Cold-shock mortality

Gonzalez Lab

anova(CSM_F_lmer_Alt_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.015338 0.015338     1 6.6251  0.3392 0.5796
anova(CSM_M_lmer_Alt_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.058672 0.058672     1 6.2882  0.6623 0.4455

Kozeretska Lab

## boundary (singular) fit: see ?isSingular
anova(CSM_F_lmer_Alt_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.042761 0.042761     1 125.6  0.3235 0.5706
## boundary (singular) fit: see ?isSingular
anova(CSM_M_lmer_Alt_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.0021266 0.0021266     1 126.38  0.0152 0.9021

Vieira Lab

anova(CSM_F_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.00015353 0.00015353     1 6.8118  0.0051 0.9451
anova(CSM_M_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Altitude 0.16878 0.16878     1 7.0405  4.5354 0.07048 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Chill-coma recovery time

Vieira Lab

anova(CCRT_F_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude  36990   36990     1 6.9544  0.2017  0.667
anova(CCRT_M_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 186.03  186.03     1 6.6486  0.0012 0.9736

Mensh Lab

Heat-shock mortality

dir.create(file.path("HeatShock"), showWarnings = FALSE)

For a detailed description of tables, plots, linear models and outputs, please refer here

Parsch Lab

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0031852 (tol = 0.002, component 1)
anova(HSM_F_lmer_Alt_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 9281.8  9281.8     1 6.9945  1.4148 0.2731
anova(HSM_M_lmer_Alt_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 9366.2  9366.2     1 7.002   1.762  0.226

Vieira Lab

anova(HSM_F_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 193.44  193.44     1 6.8578  0.0635 0.8085
anova(HSM_M_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 488.98  488.98     1 6.3519  0.1641 0.6987

Diapause

Bergland Lab

anova(Dia_lmer_Alt_Bergland)

Flatt Lab

anova(Dia_lmer_Alt_Flatt)

Schlotterer Lab

anova(Dia_lmer_Alt_Schlotterer)

Circadian eclosion timing

Locomotor activity

Tauber Lab

ND

anova(LA_lmer_Alt_ND_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.26489 0.26489     1 6.2455  1.7913 0.2274

CircPhase

anova(LA_lmer_Alt_CircPhase_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 7.3304  7.3304     1 6.7108  0.1571 0.7041

Activity

anova(LA_lmer_Alt_Activity_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.31643 0.31643     1 6.4338  0.0044 0.9492

Starvation resistance

Gonzalez Lab

anova(SR_F_lmer_Alt_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 309.49  309.49     1 6.9347  0.7251 0.4229
anova(SR_M_lmer_Alt_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 227.32  227.32     1 6.9278  1.0456 0.3409

Onder Lab

anova(SR_F_lmer_Alt_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 189.75  189.75     1 6.9417  0.5884 0.4683
anova(SR_M_lmer_Alt_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 104.15  104.15     1 6.8734  0.5309 0.4903

Pasyukova Lab

anova(SR_F_lmer_Alt_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Altitude 2203.2  2203.2     1 6.9956  4.5833 0.06957 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SR_M_lmer_Alt_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 436.78  436.78     1 6.9885  1.5442 0.2541

Pigmentation

dir.create(file.path("Pigmentation"), showWarnings = FALSE)

Abbott Lab

PercT4

anova(Pgm_lmer_Alt_Abbott_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 80.513  80.513     1 6.9765  2.6051 0.1507

PercT5

anova(Pgm_lmer_Alt_Abbott_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 74.957  74.957     1 6.9966  2.1662 0.1846

PercT6

anova(Pgm_lmer_Alt_Abbott_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 14.089  14.089     1 6.8446   0.088 0.7755

TotalPerc

Pgm_lmer_Alt_Abbott_TotalPerc <- lmer(TotalPerc ~ Altitude + (1|Population)  + (1|Line:Population) ,data = (subset(d_Pgm,Supervisor.PI=='Abbott')))
capture.output(summary(Pgm_lmer_Alt_Abbott_TotalPerc),file = "Pigmentation/Pgm_lmer_Alt_Abbott_TotalPerc_sum.txt")
capture.output(anova(Pgm_lmer_Alt_Abbott_TotalPerc),file = "Pigmentation/Pgm_lmer_Alt_Abbott_TotalPerc.txt")
anova(Pgm_lmer_Alt_Abbott_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 43.887  43.887     1 6.9567  1.2827 0.2949

Gibert Lab

PercT4

anova(Pgm_lmer_Alt_Gibert_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Altitude 350.43  350.43     1 6.3555  3.8698 0.09404 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

PercT5

anova(Pgm_lmer_Alt_Gibert_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## Altitude 979.66  979.66     1 6.3498  9.2806 0.02107 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

PercT6

anova(Pgm_lmer_Alt_Gibert_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 16.984  16.984     1 6.599  0.0817 0.7837

TotalPerc

## boundary (singular) fit: see ?isSingular
anova(Pgm_lmer_Alt_Gibert_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 195.69  195.69     1 166.39  2.6614 0.1047

Schmidt Lab

Tergite8

anova(Pgm2_lmer_Alt_Schmidt_Tergite8)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.34116 0.34116     1 6.5819  0.4746 0.5144

Tergite9

anova(Pgm2_lmer_Alt_Schmidt_Tergite9)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.045283 0.045283     1 5.4536  0.0178 0.8987

Tergite10

anova(Pgm2_lmer_Alt_Schmidt_Tergite10)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 0.83686 0.83686     1 6.2094  0.1982 0.6713

Total

anova(Pgm2_lmer_Alt_Schmidt_Total)
## Type III Analysis of Variance Table with Satterthwaite's method
##          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## Altitude 2.6253  2.6253     1 5.9721  0.1934 0.6755

Results IV : Trait Correlations tbd

Results V : Qst - Fst tbd

Results VI : Heritability tbd

  1. @ Schmidt lab, females were allowed to lay eggs for Xh, and viability is calculated as the percentage of individuals that emerged from total number of eggs, per vial.↩︎